Effect of injection timing,injector opening pressure,injector nozzle geometry,and swirl on the performance of a direct injection,compression-ignition engine fuelled with honge oil methyl ester (HOME)

Increasing petroleum prices, increasing threat to the environment from exhaust emissions and global warming have generated intense international interest in developing renewable and alternative non-petroleum fuels for engines. Evolving feasible technology and recurring energy crisis necessitated a continued investigation into the search for sustainable and clean-burning renewable fuels. In this investigation, Honge oil methyl ester (HOME) was used in a four stroke, single cylinder diesel engine. Tests were carried out to study the effect of fuel injection timing, fuel injector opening pressure (IOP) and injector nozzle geometry on the performance and combustion of CI engine fuelled with HOME. Injection timing was varied from 19°bTDC (before top dead centre) to 27°bTDC in incremental steps of 4°bTDC; injector opening pressure was varied from 210 bar to 240 bar in steps of 10 bar. Nozzle injectors of 3, 4 and 5 holes, each of 0.2, 0.25 and 0.3 mm size were selected for the study. It was concluded that retarded injection timing of 19°bTDC, increased injector opening pressure of 230 bar and 4 hole nozzle injector of 0.2 mm size resulted in overall better engine performance with increased brake thermal efficiency (BTE) and reduced HC, CO, smoke emissions. Further air-fuel mixing was improved using swirl induced techniques which enhanced the engine performance as well.     

Novel large scale simulation process to support dot’s cafe modeling system

This paper demonstrates a new process that has been specifically designed for the support of the U.S. Department of Transportation’s (DOT’s) Corporate Average Fuel Economy (CAFE) standards. In developing the standards, DOT’s National Highway Traffic Safety Administration made use of the CAFE Compliance and Effects Modeling System (the “Volpe model” or the “CAFE model”), which was developed by DOT’s Volpe National Transportation Systems Center for the 2005–2007 CAFE rulemaking and has been continuously updated since. The model is the primary tool used by the agency to evaluate potential CAFE stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The Volpe model relies on numerous technology-related and economic inputs, such as market forecasts, technology costs, and effectiveness estimates; these inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology “decision trees”. Part of the model’s function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technology to specific vehicles in its product line. A significant number of inputs to the Volpe decision-tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Argonne National Laboratory has developed a fullvehicle simulation tool named Autonomie, which has become one of the industry’s standard tools for analyzing vehicle energy consumption and technology effectiveness. Full-vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, and hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual and combined fuel-saving technologies. The Large-Scale Simulation Process accelerates and facilitates the assessment of individual technological impacts on vehicle fuel economy. This paper will show how Argonne efficiently simulates hundreds of thousands of vehicles to model anticipated future vehicle technologies.     

SiMoCo: the viability of a prototype platform for a coastal monitoring system: a case study

Coastal zones are among the most productive areas in the world, offering a wide variety of valuable habitats and ecosystems services. Despite the low population density in the Brazilian coastal zone, their distribution is quite concentrated near some coastal cities and state capitals. This concentration places enormous pressure on coastal resources. Therefore, the main objective of this paper is to present an overview on the current status of SiMoCo (Sistema de Monitoramento Costeiro, or Coastal Monitoring System in English) project as a possible early warning system that can be integrated to the Brazilian Coastal Management Information System. This prototype platform provides a real-time access to the composition, organization and simulation of planktonic communities. First, our results demonstrate such a system detecting a target dinoflagellate; second, we apply structural and functional indexes to compare and characterize the ecological networks from two different coastal areas. Conclusions are made about SiMoCo’s feasibility and its possible contribution to the decision-making process within integrated coastal zone management (ICZM) strategies.     

Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year

Using multi-day, multi-period travel diaries data of 56 days (four waves of two-week diaries) for 67 individuals in Stockholm, this study aims to examine the effects of out-of-home and in-home constraints (e.g. teleworking, studying at home, doing the laundry, cleaning and taking care of other household member[s]) on individuals’ day-to-day leisure activity participation decisions in four different seasons. This study also aims to explore the effects of various types of working schedules (fixed, shift, partial- and full-flexible) on individuals’ decisions to participate in day-to-day leisure activities. A pooled model (56 days) and wave-specific models (14 days in each wave) are estimated by using dynamic ordered Probit models. The effects of various types of working schedules are estimated by using 28 days of two waves’ data. The results show that an individual’s leisure activity participation decision is significantly influenced by out-of-home work durations but not influenced by in-home constraints, regardless of any seasons. Individuals with shift working hours engage less in day-to-day leisure activities than other workers’ types in both spring and summer seasons. The thermal indicator significantly affects individuals’ leisure activity participation decisions during the autumn season. Individuals exhibit routine behaviour characterized by repeated decisions in participating in day-to-day leisure activities that can last up to 14 days, regardless of any seasons.     

A reference-dependent user equilibrium model for activity-travel scheduling

Existing user equilibrium models of activity-travel scheduling generally fall short in representing travelers’ decision-making processes. The majority have either implicitly or explicitly assumed that travelers follow the principle of utility maximization. This assumption ignores the fact that individuals may be loss–averse when making activity-travel decisions. Allowing for the situation that travelers possess accurate information of the urban-transportation system due to modern technologies, studies on reference-dependent decision-making under near-perfect information are receiving increasing attention. In view of traveler heterogeneity, individuals can be divided into multiple classes according to their reference points. In this paper, we propose a reference-dependent multi-class user equilibrium model for activity-travel scheduling, which can be reformulated as a variational inequality problem. Moreover, comparative analyses are conducted on the equilibrium states between utility-maximization (no reference) and reference-dependency of exogenous and endogenous references. A numerical example regarding combined departure-time and mode choice for commuting is conducted to illustrate the proposed model. The simulated results indicate that reference points and loss aversion attitudes have significant effects on the choice of departure time and mode.     

New routes on old railways: increasing rail’s mode share within the constraints of the existing railway network

This paper describes an integrated methodology for identifying potential ‘quick wins’ for mode shift from road to passenger rail transport. Firstly, a procedure for analysing rail’s relative competitiveness in the market for passenger transport between large urban areas is developed and then applied to a UK case study. The purpose of such analysis is to allow the identification of flows where rail is currently relatively uncompetitive (in terms of journey time in particular) and to assess the reasons for this poor performance, so that the issues which suppress rail use may be addressed. In parallel, a framework, methodology and tool for the assessment of existing and potential capacity (trains, seats, TEUs, etc.) is developed for both passenger and freight traffic, to identify and address network constraints. An illustrative example of the use of these demand and capacity assessment tools is then presented, with the tools used to identify and evaluate flows where rail demand is suppressed by poor service quality and where spare capacity exists which would allow the passenger rail service to be improved without requiring significant investments in infrastructure. The effects of such improvements on demand are predicted, and the cost implications of operating such additional services are discussed. The analysis suggests that there may be significant potential for increasing rail’s mode share by providing additional inter-urban services where rail currently offers an inferior service.     

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.     

Quantification of motorway capacity variation: influence of day type specific variation and capacity drop

Many road authorities work with static values for road capacities, while it has been proven that capacity is not a fixed quantity. At the same time, there is an increasing need for accurate stochastic input for traffic models, such as the variation in road capacity. In this paper, a methodological framework with a conceptual model for practical stochastic capacity estimation is presented, and a quantification of motorway capacity variation is given for the influence of day‐type specific variations in capacity values. The results of the analysis show that there is a reduction in motorway breakdown capacity of 4% on weekend days in comparison with workdays. Furthermore, a capacity decrease of 8% was found for the discharge capacity in comparison with workdays. The analysis further shows that the breakdown capacity on holidays is not significantly lower than on workdays. Discharge capacity and capacity drops are also derived in each case. The results show that the capacity is significantly different depending on the type of day. A quantification of these differences is given in the form of a Weibull capacity estimation fit for each type‐of‐day scenario. Further consideration of the implications and applications of the framework is also given. Copyright © 2015 John Wiley & Sons, Ltd.     

Measurement of hydrocarbon and carbon monoxide emissions during the starting of automotive DI Diesel engines

Most of hydrocarbon (HC) and carbon monoxide (CO) emissions from automotive DI Diesel engines are produced during the engine warm-up period and are primarily caused by difficulties in obtaining stable and efficient combustion under these conditions. Furthermore, the contribution of engine starting to these emissions is not negligible; since this operating condition is highly unfavorable for the combustion progress. Additionally, the catalytic converter is ineffective due to the low engine temperature. In conjunction with adequate engine settings (fuel injection and fresh air control), either the glow plugs or the intake air heater are activated during a portion of the engine warm-up period, so that a nominal engine temperatures is reached faster, and the impact of these difficulties is minimized. Measurement of gaseous pollutants during engine warm-up is currently possible with detectors used in standard exhaust gas analyzers (EGA), which have response times well-suited for sampling at such transient conditions. However, these devices are not suitable for the measurement of exhaust emissions produced during extremely short time intervals, such as engine starting. Herein, we present a methodology for the measurement of the cumulative pollutant emissions during the starting phase of passenger car DI Diesel engines, with the goal of overcoming this limitation by taking advantage of standard detectors. In the proposed method, a warm canister is filled with an exhaust gas sample at constant volumetric flow, during a time period that depends on the engine starting time; the gas concentration in the canister is later evaluated with a standard EGA. When compared with direct pollutant measurements performed with a state-of-art EGA, the proposed procedure was found to be more sensitive to combustion changes and provided more reliable data.