Assessment of large transport infrastructure projects: The CBA-DK model

This paper presents a newly developed decision support model to assess transport infrastructure projects: CBA-DK. The model combines use of conventional cost–benefit analysis to produce aggregated single point estimates, with quantitative risk analysis using Monte Carlo simulation to produce interval results. The embedded uncertainties within traditional CBA such as ex-ante based investment costs and travel time savings are of particular concern. The paper investigates these two impacts in terms of the Optimism Bias principle which is used to take account of the underestimation of construction costs and the overestimation of travel time savings. The CBA-DK methodological approach has been used to apply suitable probability distribution functions on the uncertain parameters, thus resulting in feasibility risk assessment moving from point to interval results. The proposed assessment model makes use of both deterministic and stochastic based information. Decision support as illustrated in this paper aims to provide assistance in the development and ultimately the choice of action, while accounting for the uncertainties surrounding transport appraisal schemes. The modelling framework is illustrated by the use of a case study appraising airport and runway alternatives in the capital of Greenland – Nuuk. The case study has been conducted in cooperation with the Home Rule Authorities of Greenland.     

Calibration of a transit route choice model using revealed population data of smartcard in a multimodal transit network

Transportation - One of the major objectives of this study is to provide more realistic and accurate results related to transit passenger’s route choice behavior by using population data of...     

Voyage-based statistical fuel consumption models of ocean-going container ships in Korea

ABSTRACT

Accurately estimating fuel consumption of ships is crucial for shipping companies, port authorities, and environmental protection agencies. The bottom-up approach is becoming increasingly popular because it can estimate ship fuel consumption by accounting for ship activity conditions, such as changes in voyage speed, time, and distance; however, its use is still limited when estimating ship fuel consumption. Ship-specific information, such as the daily fuel consumption rate for main and auxiliary engines for every vessel, is expensive to gather, and generally not collected from private shipping companies. To address this research gap, we develop simplified and composite ship fuel consumption models for ocean-going container ships by size using a regression model. To estimate the fuel consumption models for container ships, we rely on ship activity data, including average speed and sailing time, distance, and actual fuel consumption for main and auxiliary engines. This information is obtained from a major container shipping company in Korea. We estimate and validate the parameters associated with fuel consumption for five different container ship sizes, all of which are smaller than the Post-Panamax container ship (15,000 TEU and above).     

A comparative study on the power characteristics and control strategies for plug-in hybrid electric vehicles

A comparative study was performed on two types of plug-in hybrid electric vehicles (PHEVs): the GM Volt and the Toyota Prius Plug-in Hybrid. First, the powertrain models of the two vehicles were derived. Based on the dynamic models, a detailed component control algorithm was developed for each PHEV. Specifically, a control algorithm was proposed for motor generator 1 (MG1) and MG2 to achieve optimal engine operation. Additionally, an energy management strategy for selecting the operation mode was developed from the viewpoint of fuel economy, battery state of charge and vehicle velocity. Using the dynamic model of the control algorithm for each PHEV, simulations were performed, and the simulation results were verified by comparing them with those obtained using the Powertrain System Analysis Toolkit simulator for the plug-in Prius. Based on the simulation results, a comparative study was performed, and it was found that the role and capacity of MG1 and MG2 and the mode selection algorithm must be determined depending on the configuration of the PHEV.     

Sensitivity analysis for reducing critical responses at the axle shaft of a lightweight vehicle

Critical responses are frequently detected at the coupled torsional beam axle (CTBA) of a lightweight vehicle. However, the freedom to modify the design of the axle shaft is limited because the suspension system must satisfy other vehicle requirements such as steering performance. Conventional sensitivity analysis cannot provide practical information about the resonant behavior because the analysis only identifies the contribution of the axle shaft to the behavior. This paper presents a novel sensitivity analysis based on transmissibility ratios (TRs). The vehicle components other than the axle shaft that can be modified to control the critical spectra are identified using acceleration responses. A multi-body vehicle model is constructed to simulate the proposed design modifications, and the simulation results show that the vibration of the axle shaft is considerably reduced by the modifications. Because the TRs on the CTBA are effectively minimized through the modified design strategy, the resonant response from the axle shaft can be controlled efficiently.     

Experimental study on the characteristics of nano-particle emissions from a heavy-duty diesel engine using a urea-SCR system

Particulate matter in diesel engine exhaust, particularly nano-particles, can cause serious human health problems including diseases such as lung cancer. Because diesel nano-particle issues are of global concern, regulations on particulate matter emissions specify that not only the weight of particulate matter emitted but also the concentration of nanoparticles must be controlled. This study aimed to determine the effects on nano-particle and PM emissions from a diesel engine when applying a urea-SCR system for NO_x reduction. We found that PM weight increases by approximately 90% when urea is injected in ND-13 mode over the emission without urea injection. Additionally, PM weight increases as the NH₃/NO_x mole ratio is increased at 250 °C. In SEM scans of the collected PM, spherical particles were observed during urea injection, with sizes of approximately 200 nm to 1 μm. This study was designed to determine the conditions under which nano-particles and PM are formed in a urea-SCR system and to relate these conditions to particle size and shape via a quantitative analysis in ND-13 mode.     

Shape optimization of rubber isolators in automotive cooling modules for the maximization of vibration isolation and fatigue life

Rubber isolators are mounted between a cooling module and a carrier to isolate the car body from vibration due to the rotation of the cooling fan. The isolators should be durable against fatigue loads originating from fan rotation and road disturbance. Thus, the design of rubber isolators is required to maximize both vibration isolation and fatigue life. In this study, the shapes of the rubber isolators are optimally designed using a process integration and design optimization (PIDO) tool that integrates the various computer-aided engineering (CAE) tools necessary for vibration and fatigue analyses, automates the analysis procedure and optimizes the design solution. In this study, we use CAE models correlated to the experimental results. A regression-based sequential approximate optimizer incorporating Process Integration, Automation and Optimization (PIAnO), a commercial PIDO tool, is employed to handle numerically noisy responses with respect to the variation in design variables. Using the analysis and design procedure established in this study, we successfully obtained the optimal shapes of the rubber isolators in two different cooling modules; these shapes clearly have better vibration isolation capability and fatigue lives than those of the baseline designs used in industry.     

Smooth motion control of the adaptive cruise control system by a virtual lead vehicle

The adaptive cruise control system maintains the appropriate distance to the lead vehicle when the lead vehicle exists and maintains the desired speed when no lead vehicle is detected. A virtual lead vehicle scheme is introduced to make the switching between the speed control algorithm and the distance control algorithm unnecessary and simplify the structure of the control system. The speed and the position of the virtual vehicle can be decided by the control system according to the current situation. Smoother responses are achieved by the virtual lead vehicle scheme compared to the conventional mode switching scheme. This method is also shown to provide a good reaction for when a lead vehicle cuts in or out. A linear quadratic controller with variable weights is suggested to control the virtual lead vehicle. This scheme shows improved performance in terms of passenger comfort and fuel efficiency of the host vehicle.     

Numerical modeling of journal bearing considering both elastohydrodynamic lubrication and multi-flexible-body dynamics

This study uses an elastohydrodynamic lubrication model coupled with multi-flexible-body dynamics (MFBD) to analyze dynamic bearing lubrication characteristics, such as pressure distribution and oil film thickness. To solve the coupled fluid-structure interaction system, this study uses an MFBD solver and an elastohydrodynamics module. The elastohydrodynamics module passes its force and torque data to the MFBD solver, which can solve general dynamic systems that include rigid and flexible bodies, joints, forces, and contact elements. The MFBD solver analyzes the positions, velocities, and accelerations of the multi-flexible-body system while incorporating the pressure distribution results of the elastohydrodynamics module. The MFBD solver then passes the position and velocity information back to the elastohydrodynamics solver, which reanalyzes the force, torque, and pressure distribution. This iteration is continued throughout the analysis time period. Other functions, such as mesh grid control and oil hole and groove effects, are also implemented. Numerical examples for bearing lubrication systems are demonstrated.     

Expanding transmission capacity of can systems using dual communication channels with kalman prediction

The controller area network (CAN) protocol is widely used for in-vehicle network (IVN) systems, and many automotive companies also use the CAN in chassis network systems. However, the increasing number of electronic control units (ECUs) dictated by the need for more intelligent and fuel-efficient functions requires an IVN system with a greater transmission capacity and less network delay. Automotive companies have tried several approaches such as segmenting CAN systems and developing time-triggered protocols. This paper presents a practical method for increasing the transmission capacity and reducing the network delay in CAN systems using dual communication channels with a traffic-balancing algorithm based on Kalman prediction to forecast the traffic on each channel and allocate frames to the one that is most appropriate. An experimental testbed using commercial microcontrollers with two or more CAN protocol controllers was used to demonstrate the feasibility of the Kalman traffic-balancing algorithm. Experimental results show that the traffic-balancing CAN system with Kalman prediction reduced the transmission delay of all priority messages compared to that of a simple method, such as a channel-switching CAN, without sacrificing the performance for high-priority messages.