基于发动机热力过程的汽车排气消声器设计

基于发动机的热力循环理论和管道声学理论，利用AVL公司的B(X)ST软件联合建立了发动机和消声器的仿真模型，在此基础上进行了Flyer轿车的排气消声器设计，试验证明这种设计方法能够充分考虑发动机的综合性能和消声性能。     

钢管混凝土拱空间极限承载力高精度分析

采用考虑材料非线性的钢管混凝土拱空间极限承载力计算方法对1个X型双肋拱与1个平行双肋拱进行了空间极限承载力计算。在该方法中，对钢管混凝土拱结构采用纤维单元模型，该模型假定钢管与混凝土完全粘接，钢管对核心混凝土的套箍作用体现在以一维形式表达的核心混凝土的应力-应变关系曲线之中，针对材料非线性分析中单元内各点刚度参差不齐的特点，采用单元内设小元的方法(相当于子结构)，编制了非线性有限元程序，在该程序中，计算模型完全是基于小元层次进行的，比如单元刚度矩阵由小元刚度矩阵凝聚而成，单元节点的残余力由小元节点的残余力构成，故只需改变单元内小元个数这1个参数就可实现对结构的重新划分且极大地降低了非线性方程组的阶数，非常方便且实用。在程序计算结果得到模型试验结果验证的基础上，还对不同的横撑根数对结构空间极限承载力的影响进行了分析。     

高液限土的改良技术研究

高液限土主要分布于我国南方地区,是公路修筑中常见的特殊土之一,我国有关规范规定其不得直接用于路基的填筑。本文对广东某高速公路的高液限土进行了详细的室内稳定剂改良试验,得出了不同处治方法的改良效果。铺筑了采用康耐稳定剂改良的高液限土试验路,并进行了相应的观测,初步明确了康耐稳定剂改良高液限土路基的稳定效果。     

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.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

Life cycle emissions and cost model for urban light duty vehicles

The growth of vehicle sales and use internationally requires the consumption of significant quantities of energy and materials, and contributes to the deterioration of air-quality and climate conditions. Advanced propulsion systems and electric drive vehicles have substantially different characteristics and impacts. They require life cycle assessments and detailed comparisons with gasoline powered vehicles which, in turn, should lead to critical updates of traditional models and assumptions. For a comprehensive comparison of advanced and traditional light duty vehicles, a model is developed that integrates external costs, including emissions and time losses, with societal and consumer life cycle costs. Life cycle emissions and time losses are converted into costs for seven urban light duty vehicles. The results, which are based on vehicle technology characteristics and transportation impacts on environment, facilitate vehicle comparisons and support policy making in transportation. Substantially, more sustainable urban transportation can be achieved in the short-term by promoting policies that increase vehicle occupancy; in the intermediate-term by increasing the share of hybrid vehicles in the car market and in the long-term by the widespread use of electric vehicles. A sensitivity-analysis of life cost results revealed that vehicle costs change significantly for different geographical areas depending on vehicle taxation, pricing of gasoline, electric power and pollution. Current practices in carbon and air quality pricing favor oil and coal based technologies. However, increasing the cost of electricity from coal and other fossil fuels would increase the variable cost for electric vehicles, and tend to favor the variable cost of hybrid vehicles.     

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.     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

Traffic modelling in system boundary expansion of road pavement life cycle assessment

This paper uses a case study of a UK inter-urban road, to explore the impact of extending the system boundary of road pavement life cycle assessment (LCA) to include increased traffic emissions due to delays during maintenance. Some previous studies have attempted this but have been limited to hypothetical scenarios or simplified traffic modelling, with no validation or sensitivity analysis. In this study, micro-simulation modelling of traffic was used to estimate emissions caused by delays at road works, for several traffic management options. The emissions were compared to those created by the maintenance operation, estimated using an LCA model. In this case study, the extra traffic emissions caused by delays at road works are relatively small, compared to those from the maintenance process, except for hydrocarbon emissions. However, they are generally close to, or above, the materiality threshold recommended in PAS2050 for estimating carbon footprints, and reach 5–10% when traffic flow levels are increased (hypothetically) or when traffic management is imposed outside times of lowest traffic flow. It is recommended, therefore, that emissions due to traffic disruption at road works should be included within the system boundary of road pavement LCA and carbon footprint studies and should be considered in developing guidelines for environmental product declarations of road pavement maintenance products and services.     

Integrating capacity analysis with high-speed railway timetabling: A minimum cycle time calculation model with flexible overtaking constraints and intelligent enumeration

Compared with most optimization methods for capacity evaluation, integrating capacity analysis with timetabling can reveal the types of train line plans and operating rules that have a positive influence on improving capacity utilization as well as yielding more accurate analyses. For most capacity analyses and cyclic timetabling methods, the cycle time is a constant (e.g., one or two hours). In this paper, we propose a minimum cycle time calculation (MCTC) model based on the periodic event scheduling problem (PESP) for a given train line plan, which is promising for macroscopic train timetabling and capacity analysis. In accordance with train operating rules, a non-collision constraint and a series of flexible overtaking constraints (FOCs) are constructed based on variations of the original binary variables in the PESP. Because of the complexity of the PESP, an iterative approximation (IA) method for integration with the CPLEX solver is proposed. Finally, two hypothetical cases are considered to analyze railway capacity, and several influencing factors are studied, including train regularity, train speed, line plan specifications (train stops), overtaking and train heterogeneity. The MCTC model and IA method are used to test a real-world case involving the timetable of the Beijing–Shanghai high-speed railway in China.