冲压机械振动对高铁桥梁及行车动力影响分析

高速铁路桥梁的平顺性和稳定性对运营列车的平稳性和安全性有很大影响。为研究冲压机械产生的外部振动激励对高铁桥梁的影响,首先通过对此机械引起的地面振动进行实测,并结合有限元分析软件,确定最大冲击荷载作用下产生的地面振动及传播至桥墩处的振动;然后通过建立列车-轨道-桥梁耦合动力学模型,将桥墩处的地面振动作为激励输入,分析列车以不同速度通过时车辆、桥梁动力学响应。结果表明:地面冲击振动有限元模型计算结果与实测结果基本相符,验证了模型的可靠性;地面振动对桥梁响应会产生一定的影响,距振源50 m处地面振动对桥梁所产生的影响较距振源80 m处(桥墩处)的大,但对运行车辆的影响很小;随着车速由250 km/h至350 km/h,车辆及桥梁各结构的动态响应均有所增大,但都未超出安全限值。因此,冲压机械冲击作用导致的地面振动对列车-轨道-桥梁系统动态服役性能影响非常有限。     

喷孔位置对船用天然气发动机燃烧及排放影响的模拟研究

为了探究天然气与柴油喷孔位置对天然气发动机燃烧和排放的影响,基于L23/30天然气发动机,建立了柴油引燃天然气的发动机模型,利用CONVERGE软件对燃烧过程和排放进行模拟分析。模拟结果表明：由于天然气与柴油喷孔位置变化的影响,会导致柴油引燃天然气在空间的变化,因此对发动机的燃烧和排放造成了影响,通过对比不同喷嘴间的距离和不同喷嘴分布的设计方案得出,采用中心对称设计时获得了较好的燃烧和排放性能。     

机车自动驾驶系统模式切换策略研究

受铁路基础装备技术和国内复杂运用环境限制,机车自动驾驶系统仍需在有人值守的情况下工作,所以应有完善的自动驾驶系统模式切换策略来界定值守人员和自动驾驶的权限边界。以机车运行安全为原则,基于列车纵向动力学分析动态切换时列车的平稳性,阐述了机车自动驾驶系统的模式切换策略,该策略已经运用于机车自动驾驶实际应用中。大量实践案例证明,提出的机车自动驾驶模式切换策略能够有效保证机车控制权模式切换过程中列车的安全、平稳运行,取得了良好的运行效果。     

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.     

高桩码头水上施工平台的设计计算

通过不同施工阶段、不同荷载形式，对高桩码头水上施工平台的主梁、次梁及牛腿等进行了受力计算，其强度、刚度及安全性能均满足规范及使用要求，保证了平台的安全性与稳定性。钢平台的搭设，确保了钻孔嵌岩灌注桩及其上部结构的顺利施工。     

How to Use Driving Simulators Properly: Impacts of Human Sensory and Perceptual Capabilities on Visual Fidelity

Fidelity has been a critical concern of researchers throughout the history of driving simulation. Understanding the limits of a driving simulation system is a prerequisite for conducting valid driving simulator studies. This paper proposes a novel and interdisciplinary methodology to ensure validity of studies using driving simulators (primarily for traffic control devices and other object detection tasks) based on the visual limits of human sensory and perceptual capabilities, and the characteristics of raster graphics. This methodology decomposes the perceptual issues of a stimulus into perceptual issues of different visual properties like luminance, hue, or text of the stimulus. By systematically analyzing the mechanism of human vision in driving simulators, the perceptual principle is proposed to ensure perceivable visual details in human-in-the-loop driving simulation systems. Additionally, the graphic principle is proposed to ensure perceivable features of a target object in the virtual driving environment. Both principles quantify the minimum requirements of visual fidelity with two measurements: angular resolution and matrix dimensions. The enriched results from existing pertinent studies are analyzed and organized to yield support of both principles. This research focuses on the minimum requirements for four factors; namely the visual acuity of drivers, the specifications of display systems, the configurations of graphics systems, and the design of virtual scenarios, as well as the relationship among all these factors to assess the visual fidelity in driving simulation systems. Within the realm of human perception, this work can provide criteria for proper design, calibration, and usage of driving simulators.     

Field tests of a dynamic green driving strategy based on inter-vehicle communication

This paper presents the design and results for field tests regarding the environmental benefits in stop-and-go traffic of an algorithmic green driving strategy based on inter-vehicle communication (IVC), which was proposed in Yang and Jin (2014). The green driving strategy dynamically calculates advisory speed limits for vehicles equipped with IVC devices so as to smooth their speed profiles and reduce their emissions and fuel consumption. For the field tests, we develop a smartphone-based IVC system, in which vehicles’ speeds and locations are collected by GPS and accelerometer sensors embedded in smartphones, and communications among vehicles are enabled by specially designed smartphone applications, a central server, and 4G cellular networks. Six field tests are carried out on an uninterrupted ring road under slow or fast stop-and-go traffic conditions. We compare the performances of three alternatives: no green driving, heuristic green driving, and the IVC-based algorithmic green driving. Results show that heuristic green driving has better smoothing and environmental effects than no green driving, but the IVC-based algorithmic green driving outperforms both. In the future, we are interested in field tests under more realistic traffic conditions.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

港口高杆灯的无线智能控制系统设计

随着港口建设规模的扩大,港区堆场面积越来越大,配套的高杆灯及其他照明设施的数量也相应增加,需要一个自动化的、科学的管理方式和系统对其进行管理和控制。照明无线智能控制系统采用先进可靠的远距离无线通信技术,将人机操作界面和远程控制环节有机结合起来,实现对高杆灯的远距离无线控制。通过高杆灯无线智能控制系统,合理组合开启灯具数量和安排开启时间,智能调光,以达到高效节能、安全运行的良好效果。     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.