预喷射对轻型车用柴油机排放性能的影响

以某轻型车用柴油机为样机,研究了预喷射对柴油机排放及燃油经济性的影响.研究表明:采用预喷射可以对燃烧室进行预热.缩短主燃烧滞燃期,降低缸内最高燃烧温度,从而降低NOx的排放,同时炭烟排放及燃油经济性要好于单纯推迟喷油提前角的单次喷射方案;随着预喷油量的增加,NOx炭烟和燃油消耗率都有所上升,预喷提前角对轻型车用柴油机燃...     

内燃机进排气系统的数值模拟

随着排放法规的日趋严格和能源的日益紧缺,发动机燃烧过程的优化显得尤为重要。文章利用GT-Power软件建立了某发动机工作过程的仿真模型,应用总变差减少的有限体积法（FVM—TVD）对该发动机的进排气系统进行数值模拟,包括进排气管内流体流速随曲轴转角变化的情况,以及进排气管内的压力波动情况。计算结果表明,动态效应对进排气管内的宏观气流运动有较大的影响。在压力波动和脉动效应等因素的作用下,进排气管内流体的流速和流量均呈波动状变化。     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

Decomposing passenger transport futures: Comparing results of global integrated assessment models

The transport sector is growing fast in terms of energy use and accompanying greenhouse gas emissions. Integrated assessment models (IAMs) are used widely to analyze energy system transitions over a decadal time frame to help inform and evaluating international climate policy. As part of this, IAMs also explore pathways of decarbonizing the transport sector. This study quantifies the contribution of changes in activity growth, modal structure, energy intensity and fuel mix to the projected passenger transport carbon emission pathways. The Laspeyres index decomposition method is used to compare results across models and scenarios, and against historical transport trends. Broadly-speaking the models show similar trends, projecting continuous transport activity growth, reduced energy intensity and in some cases modal shift to carbon-intensive modes - similar to those observed historically in a business-as-usual scenario. In policy-induced mitigation scenarios further enhancements of energy efficiency and fuel switching is seen, showing a clear break with historical trends. Reduced activity growth and modal shift (towards less carbon-intensive modes) only have a limited contribution to emission reduction. Measures that could induce such changes could possibly complement the aggressive, technology switch required in the current scenarios to reach internationally agreed climate targets.     

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO₂ emissions effectively.     

Help or hindrance? The travel,energy and carbon impacts of highly automated vehicles

Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.     

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.     

The significant impacts on traffic and emissions of ferrying children to school in Beijing

After having implemented numerous regulations, e.g., coercive policies on vehicle use and purchase, it is becoming increasingly difficult to find further potential to control vehicle emissions in Beijing, as the air quality is still poor. This research provides a different approach for policy-makers to reduce vehicle emissions by managing demand. We found that parents ferrying their children to and from school is an important but long-neglected contributor to traffic congestion and vehicle emissions. This phenomenon is very common in China because of the social culture. In this research, parallel tests during both the school season and the non-school season were adopted, and emissions in both seasons were calculated based on travel demand and emission models. The results revealed that emissions factors (in g/km) for criteria pollutants and CO₂ increased by over 10% during rush hours during the school season due to traffic condition deterioration compared with non-school season. Daily HC, CO, NO_x, PM and CO₂ emissions from the passenger car fleet were 8.3%, 7.8%, 6.4%, 6.3% and 6.5% higher compared with those during the non-school season, respectively. These differences are greater than the total vehicular emission reduction by other control measures in 2014 in Beijing. For policy makers, providing safe and efficient ways to ferry children would be a useful and harmonious strategy for future vehicle emission control.     

A bibliometric analysis on trends and characters of carbon emissions from transport sector

Transport sector’s substantial contribution to global greenhouse gas emissions has made it a growing area of study and concern. In order to identify trends and characteristics of carbon emissions research in the transportation sector we conducted a Bibexcel and complex network analysis for the period 1997–2016. In addition, we identify critical themes and contributions of research articles using h-index, PageRank and cluster analysis. We report contribution of countries, authors, institutions and journals, as well as performance of citations and keywords. Co-citing situations between different countries, authors, and institutions are also analyzed using network analysis. Between 1997 and 2016 we found a rise in publications on carbon emissions in the transportation sector and increased cooperation between countries, authors, and institutions. Authors from the USA, China and United Kingdom published the most articles and articles with the highest academic influence. Tsinghua University from China is the leading institution in carbon emissions research in the transportation sector. The most widely published author and cited author is Dr. He. We conclude our analysis by analyzing keywords and trends to suggest critical topic areas of future research. The systematic approach undertaken in this study can be extended and applied to other research topics and fields.     

上海轨道交通12号线通风空调系统设计

通风空调系统运行能耗巨大,其设计是城市轨道交通系统总体设计中的重中之重。基于相关标准和节能减排要求,确定了上海轨道交通12号线工程通风空调系统的运行模式,并对区间隧道(含辅助线)通风系统兼排烟系统、车站车轨区排热兼排烟系统、车站公共区通风空调系统、设备管理用房通风空调系统、空调冷源及水系统的设计进行了详细阐述。