应用线弹性疲劳损伤理论的沥青路面轴载换算

应用疲劳损伤力学基本原理,探讨了适用于沥青路面轴载换算的新方法,并对该方法拟合的公式与规范按弯沉等效推荐的当量轴载换算公式进行了比较。计算结果表明在进行沥青路面轴载换算时,采用不同的等效换算原理,将导致非标准轴载与标准轴载当量换算系数上的不同,前者较后者大。这种差异一方面直接影响沥青路面的结构厚度设计,对设计年限内累计当量标准轴载作用次数的低估,必然导致沥青路面早期破损现象的发生;另一方面揭示弯沉等效表征的是路面结构的瞬间刚度性质,代替不了路面结构的长期疲劳强度特性。     

赣江大桥公路桥病害检测与安全评估

首先对赣江大桥公路桥进行病害调查,评定全桥各部位损伤状态。根据评定结果,再对材料退化、结构损伤与受力性能进行实桥测试。应用断裂力学方法,采用观测和超声波探测方法确定初始裂纹尺寸,通过裂纹扩展模拟得出临界杆件的剩余寿命。综合实测数据与理论分析,评估该桥使用安全性和剩余寿命,并建议维护加固措施。     

How the cap limit for food-crop-based biofuels may affect France’s stakeholders by 2030? A range-based multi-actor multi-criteria analysis

From 2012 to 2016, the long-term signal in the biofuel market changed almost once per year, leading to a drastic decrease in investments and contributing to multiple production unit closures in France and around Europe. The European Commission proposed a new renewable energy directive that includes a 3.8% cap limit on the contribution of food-crop biofuels by 2030. Given the role of biofuels in green growth, the bioeconomy, and renewable energy incorporation targets, how will this measure affect the stakeholders by 2030? Will it lead to, contribute to, or hamper their sustainability criteria? This paper aims to contribute to this debate by studying the case of France. To this end, our methodology—the range-based multi-actor multi-criteria analysis—aims to (1) explicitly consider the stakeholder groups and their sustainability criteria; (2) evaluate and compare how the cap limit will affect these sustainability criteria whether or not advanced biofuels are deployed by 2030; (3) capture the uncertainty of the context evolution and biofuel capacity to fulfil the stakeholders’ sustainability criteria by means of a Monte Carlo. The results suggest that the cap limit is a double-edged sword for the stakeholders and their sustainability criteria. Shifting towards advanced biofuels while limiting the food-crop biofuels is the better alternative for most of the stakeholders. Nevertheless, given biofuel policy instability and the lost confidence of investors, such a shift may not occur by 2030. In such a case, this paper demonstrates that the cap limit may highly and negatively affect the stakeholders and their sustainability targets, whereas fostering French food-crop biofuel production at its full capacity level constitutes a better alternative. As no alternative is suited to all actors simultaneously, this paper also studies the strengths and weaknesses of these alternatives from each stakeholder groups’ perspective.     

Fitness-for-purpose assessment of cracked offshore wind turbine monopile

In this paper, the procedure for flaw acceptability assessment is examined through a case study of a semi-elliptical surface crack in an offshore monopile as it grows till it forms a through thickness crack. Using the procedure prescribed in an industrial standard (BS 7910), the fracture ratio, K_r is shown to increase monotonically with increasing crack depth. The load ratio, L_r, is initially insensitive to the crack depth. However, there is a rapid increase in L_r when the crack depth to thickness ratio exceeds 80%. L_r values obtained from detailed 3D FE limit analysis using elastic-perfectly-plastic material behaviour do not exhibit the asymptotic behaviour predicted by BS 7910 as the flaw transitions from deep crack to through-thickness crack. Furthermore, K_r predicted by BS 7910 is shown to be an over-estimation for the typical dimensions of offshore monopiles. The findings suggest that a structure with a deep flaw may be identified as unacceptable based on BS 7910 when it may still possess a non-trivial amount of structural residual life. This is a concern for monopiles where crack growth as a large flaw forms a significant part of the total life.     

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.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

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.