Effect of fuel stratification on initial flame development: Part 3-high swirl condition

This paper discusses the final investigation into the effect of fuel stratification on flame propagation. In previous works, the characteristics under the no port-generated swirl condition and the low-swirl condition were considered. For this purpose, the initial flame development and propagation were visualized under different axially stratified states in a modified optical single-cylinder SI engine. The images were captured by an intensified CCD camera through the quartz window mounted in the piston. Stratification was controlled by the combination of the port swirl ratio and injection timing. These were averaged and processed to characterize the flame propagation. The flame stability was estimated by the weighted average of flame area and luminosity. The stability was also evaluated through the standard deviation of flame area and propagation distance and through the mean absolute deviation of the propagation direction. The results show that the LML is expanded remarkably under the high-swirl cases up to the highest relative AFRs of 1.71 and 1.75 between 140 and 160CA. In addition, similar to the low-swirl condition, the flame-flow interaction determines the direction of flame propagation, and the governing roles of the two factors vary according to the swirl level; the flow is more important at the higher swirl conditions, and the flame is more important at the lower swirl condition. Finally, fast and stable flame propagation can be achieved under the preferably stratified condition, which is induced by the suitable combination of the high swirl and injection timing.     

Experimental Investigation of TARMAX Model for Modeling of Hydrodynamic Forces on Cylinder-like Structures

A new approach that models lift and drag hydrodynamic force signals operating over cylindrical structures was developed and validated. This approach is based on stochastic auto regressive moving average with exogenous (ARMAX) input and its time-varying form, TARMAX. Model structure selection and parameter estimation were discussed while considering the validation stage. In this paper, the cylindrical structure was considered as a dynamic system with an incoming water wave and resulting forces as the input a...     

船用柴油机组调速系统仿真研究

模拟电力系统的模拟研究中,柴油机及其调速系统直接影响系统机电暂态过程的准确性,对于判别系统受到大扰动后的暂态稳定和受到小扰动后的静态稳定性具有重要意义。从电站柴油发电机组的运动方程出发,在柴油机及其调速器数学模型的基础上,利用数字仿真计算机实时计算模拟转矩指令,用以控制一套高性能交流调速系统,从而实现电站柴油发电机组中柴油机及其调速器与系统转动惯量的数模混合模拟。实验表明,该模拟系统通用性强,响应速度快,准确性较高。     

A multiobjective chance constrained programming model for supplier selection under uncertainty

Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.     

Approximate network loading and dual-time-scale dynamic user equilibrium

In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that Pang and Stewart (2008) refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of Friesz et al. (1993) may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to Daganzo (1995). We provide a numerical example based on the much studied Sioux Falls network.     

Bus congestion, optimal infrastructure investment and the choice of a fare collection system in dedicated bus corridors

Microeconomic optimisation of scheduled public transport operations has traditionally focused on finding optimal values for the frequency of service, capacity of vehicles, number of lines and distance between stops. In addition, however, there exist other elements in the system that present a trade-off between the interests of users and operators that have not received attention in the literature, such as the optimal selection of a fare payment system and a designed running speed (i.e., the cruising speed that buses maintain in between two consecutive stops). Alternative fare payment methods (e.g., on-board and off-board, payment by cash, magnetic strip or smart card) have different boarding times and capital costs, with the more efficient systems such as a contactless smart card imposing higher amounts of capital investment. Based on empirical data from several Bus Rapid Transit systems around the world, we also find that there is a positive relationship between infrastructure cost per kilometre and commercial speed (including stops), achieved by the buses, which we further postulate as a linear relationship between infrastructure investment and running speed. Given this context, we develop a microeconomic model for the operation of a bus corridor that minimises total cost (users and operator) and has five decision variables: frequency, capacity of vehicles, station spacing, fare payment system and running speed, thus extending the traditional framework. Congestion, induced by bus frequency, plays an important role in the design of the system, as queues develop behind high demand bus stops when the frequency is high. We show that (i) an off-board fare payment system is the most cost effective in the majority of circumstances; (ii) bus congestion results in decreased frequency while fare and bus capacity increase, and (iii) the optimal running speed grows with the logarithm of demand.     

Capacity drops at merges: An endogenous model

The Newell-Daganzo merge model is not only very simple but also accurately reproduces experimental findings. However, the capacity downstream of the merge is an exogenous variable in the model. This is a serious limitation for merges that behave as active bottlenecks because their downstream capacity is a direct consequence of the merging behavior. This paper proposes an analytical model that extends the Newell-Daganzo model by incorporating, endogenously, the capacity drop related to the merging process. Two cases are investigated depending on the traffic states on the on-ramp. The model properties are analyzed and a sensitivity analysis is performed to quantify the relative contribution of the each parameter in the capacity drop. Finally, the extended Newell-Daganzo model is validated with experimental data coming from an active merge bottleneck on the M6 freeway in UK.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Timetable-based operation in urban transport: Run-time optimisation and improvements in the operating process

Urban public transit provides an efficient means of mobility and helps support social development and environmental preservation. To avoid loss of ridership, transit authorities have focussed on improving the punctuality of routes that operate using timetables. This paper presents a new approach to generating run-time values that is based on analytical development and micro simulations. The work utilizes previous research (described herein) and the experience acquired by Transports Metropolitans de Barcelona (TMB) in operating bus routes based on timetables. Using a sample of historical data, the method used for generating run-time values consists of the following steps: purging and screening atypical trips, based on the consideration of confidence intervals for median trips; segmenting the day into time bands based on the introduction of a new hierarchical classification algorithm; creating initial run-time values based on criteria derived from statistical analysis; adjusting and validating initial run-time values using micro simulations; and evaluating incident-recovery times at the end of trips in order to guarantee the punctual departure of the next trip in the vehicle schedule. To favour service improvement, we also introduced certain indicators that can identify the root causes of non-compliance. As a final step, in order to ensure the applicability and use of the model, we promoted the development of our model within the framework of the HASTUS™ software solution.     

Seaport research: A structured literature review on methodological issues since the 1980s

This study aims to investigate how seaport research has been conducted from the methodological perspective. To this end, this study reviews published port literature for the last three decades (1980-2000s). The investigation primarily categorises the literature according to various methodological issues such as research paradigm, research strategy, base-disciplines, research methods and analysis techniques in order to provide meaningful implications on methodological evolution in seaport research for the period. This study suggests methodological bias in port research to a positivistic paradigm, following a quantitative trajectory moving from conceptual to empirical studies. In addition, the increased use of mathematical modelling and advanced statistical analysis methods is clearly observed. The introduction of advanced analytical tools used in other academic disciplines facilitates discussions in particular research area and amplifies the literature in those areas. This paper also suggests research gaps from the methodological perspective and implications for future port research.