Finite-difference simulation of green water impact on fixed and moving bodies

A numerical method was developed to predict the water impact pressure caused by green water phenomena. The density function method was employed in the framework of a locally refined overlapping grid system. The simple problem of a rectangular body placed in regular waves was considered and the simulation results were compared with tank experiments. Good agreement with experiment was shown for wave–body interactions and for the pressure values at three different positions on the body. The case of a rectangular body with a vertical wall on the deck was also considered and the comparison showed satisfactory agreement. It was demonstrated that this method could be extended to a moving body problem, in which the body was free to undergo heave, pitch, and surge motions.     

单相交流牵引供电系统关键技术现状及发展

介绍各国主要单相交流牵引供电制式,总结各国为了解决制约单相交流牵引供电系统发展的电分相问题所采取的多种过分相方式,为了解决电能质量问题所采取的各有侧重点的治理措施,对比国内外同相供电技术并指出国外技术中值得我国借鉴之处,最后基于已有关键技术并结合储能和新能源等背景描绘了"绿色"牵引供电系统,为单相交流牵引供电系统的发展提供新思路。     

工程机械底盘液压驱动装置性能分析(1)

液压驱动装置的结构形式与性能是工程机械底盘液压驱动与控制的两大重要组成部分之一。分析讨论了各类结构形式的液压驱动装置及其与行走机构组成不同形式的液压车辆底盘时的性能与特点。对车辆工作所需要的特殊功能，如液压同步、限速、制动以及补油回路等也进行了讨论，有助于工程车辆的理论研究与产品设计。     

发动机冷却风扇温控液力驱动系统

本文介绍了发动机冷却风扇温控系统，这种新型的控制系统具有自动测量，微机控制，液压驱动，无级变速等特点，可以使发动机在最佳温度下工作，文中详细介绍了系统构成，测控元件选择及软件设计。     

接地面部分溶化条件下冰面轮胎摩擦力的研究

综合运用流体动力润滑理论的能量守恒定律，提出了接地面部分溶化条件下冰面上汽车轮胎的摩擦力或牵引力的理论计算模型。证明了正常工作条件下，接地面上冰产生溶化的可能。对不同制动和驱动条件下轮胎摩擦力进行了计算，取得了同试验数据吻合很好的预测结果。并同已有预测模型进行了比较，比较结果表明：本文依据严格的润滑理论所推导出的冰面轮胎牵引力模型比其它已有理论模型更接近试验结果。     

交叉口单点公共汽车交通优先控制方法研究

开发ITS背景下的公共汽车交通优先控制方法是从根本上改善公交车辆运行情况的需要。满足车辆固有的行驶时刻表是实施公交优先通行申请的重要依据,在本文控制策略中运用绿灯时间延长和相位提前激活两种方法,在兼顾其它车辆运行情况的前提下,给予晚点公交车优先的通行信号。论文最后以厦门市湖滨南路实际交通流量为依托,运用交通仿真方法验证了优先控制策略的实际效果。     

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.     

Congestion and pollution consequences of driving-to-school trips: A case study in Beijing

Parents compete for high-quality education for their children by enrolling them in good schools. However, in a Chinese mega-city like Beijing, three factors jointly lead to the spatial separation between schools and homes: the centralized public goods provision mechanism, the historical dependency in school location, and the constrained supply of housing in downtown. Without an adequate number of school buses, this spatial separation of schools and homes triggers the numerous long-distance driving-to-school trips by private vehicle during workday morning rush hours in Beijing. We use the start and end dates of “school holiday” as exogenous repeated shocks to the aggregate traffic congestion, and employ the two-stage least squares (2SLS) regression approach to examine the congestion and pollution consequences of such driving-to-school trips in Beijing. We find that, all else being equal, workdays during school holidays have a traffic congestion index 20% lower than that of non-school-holiday workdays. Such a sharp reduction in congestion leads to a significant decrease in PM₁₀ concentration. Policymakers should lower such “extra” congestion and environmental costs via optimizing the spatial balance between school supply and demand.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.