Simulation of flow in diesel particulate filter system using metal fiber filter media

Metal fiber is introduced as a new filter media in wall-flow Diesel Particulate Filter (DPF) system. This technology has high temperature durability which is required for filter regeneration, and can maintain the mechanical strength even in the extreme exhaust-related vibrations of vehicles. However, the regeneration near the wall (outer layer) is more difficult because of the heat loss and reduced gas flow near the wall. In this study, a flow is simulated to determine the flow control method for the more uniform flow in all filter layer. By using Star CCM+ commercial software, we obtain local velocity, streamline, and pressure distributions in the filter, which are typically difficult to obtain from measurements. The major control factors are the filter porosity, size and location of the distribution plate, and the number of blades of the swirler. By placing the distribution plate in front of the filter, the flow velocity near the wall was increased. The optimum location and size of the flat plate were chosen. By attaching the blade on the plate the flow velocity near the wall was increased more. Therefore, the regeneration efficiency is increased by using the swirler-type metal fiber DPF system.     

An experimental study of the effect of low-noise wheels in reducing noise and vibration

Wheel/rail interaction is a major source of railway noise. A low-noise wheel structure is developed and its effect on noise reduction is investigated. This low-noise wheel employs a rubber material inserted into the steel rim or mounted on the wheel surface. The low-noise wheel has low stiffness and a high-damping ratio compared to a solid wheel. Measurement shows that it reduces rolling and squealing. It turns out that a subway line with the proposed wheel could reduce its interior noise level by 4–5 dB(A) and vehicle vibration level by 7–8 dB. While the proposed structure seems promising in noise reduction for railway vehicles, the endurance and cost effectiveness of the low-noise wheel are yet to be verified.     

Numerical simulation of three-dimensional breaking waves

Three-dimensional (3D) wave breaking around bodies of complex geometry has been numerically investigated by use of two types of Navier-Stokes solvers, namely the finite-difference and the finite-volume methods employing rectangular and curvilinear coordinate systems, respectively. Both methods employ the density-function technique to capture the free surface location and can cope with complicated free surface configurations such as breaking waves. The accuracy of the density-function method is examined through the comparison with experimental results, and it is confirmed to be satisfactory when the grid spacing and the time increment are sufficiently small. New computational methods are applied to several problems including 3D breaking waves around ships and wave diffraction around offshore structures. The computed results show good agreement with experimental results indicating that wave breaking phenomena are successfully simulated. The qualitative accuracy, however, could be improved by including the dissipating effect of breaking waves.     

Ship-berth link performance evaluation: simulation and analytical approaches

In this paper we consider the performance evaluation of ship-berth link in port. The efficiency of operations and processes on the ship-berth link has been analysed through the basic operating parameters such as berth utilization, average number of ships in waiting line, average time that a ship spends in waiting line, average service time of a ship, average total time that a ship spends in port, average quay crane (QC) productivity and average number of QCs per ship. All the main performances of the ship-berth link are given. This is one of the problems faced by planners and terminal operators in ports. In this paper, we propose two models based on simulation and queuing theory, respectively, in order to determine the performance evaluation of ship-berth link in port. Numerical results and computational experiments are reported to evaluate the efficiency of the models for Pusan East Container Terminal (PECT).     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

Neural network-based fuel consumption estimation for container ships in Korea

ABSTRACT

Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.     

Vehicle distance estimation using a mono-camera for FCW/AEB systems

For robust vision-based forward collision warning (FCW) and autonomous emergency braking (AEB) systems, not only reliable detection performance including high detection rate and low false positives but also accurate measurement output of a target vehicle is required. Especially, in order to reduce false alarm or activation of FCW/AEB systems, the systems require the precise measurement output of a target object, such as position, velocity, acceleration, and time-to-collision (TTC). In this study, we developed a measurement estimation algorithm of a target vehicle using a monocular camera. This method estimates two cases of vehicle widths for a target vehicle by using the detected lane information and a pin-hole camera model. After that, the position, velocity, acceleration, and TTC of a target vehicle are estimated by using a Kalman filter for the each estimated vehicle width. To improve robustness, the both estimation results using the detected lane information and the pinhole camera model are fused. This estimation algorithm was evaluated and compared with the state-of-the-art technology. As a result, the proposed measurement output estimation method can improve the performance of the FCW/AEB systems.     

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.     

Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

In designing a parallel hybrid electric vehicle, it is essential to select the optimal capacity of power sources and the optimal gear ratio of the torque coupler. The capacity of the power sources and the gear ratio of the torque coupler should be optimized simultaneously. However, since this process is excessively time-consuming, previous studies have selected the gear ratio of the torque coupler and then selected the capacity of power source. However, this approach cannot guarantee global optimization. In this paper, a feasible region is defined to satisfy the required performance of vehicle such as maximum speed, hill-climbing. and feasible points are selected inside the feasible region. In the conventional technique, the global optimal solution is obtained by simulating all feasible points. In the optimization technique, optimal points are simulated within the feasible region using several optimal search algorithms, such as the golden section search algorithm and the hillclimbing search algorithm. And using these algorithms, the number of simulations is reduced and the capacity of the power source and the gear ratio of the torque coupler are optimized simultaneously. Finally, the validity of the component sizing results is verified by comparing the global optimal solution obtained by applying the conventional technique with the solution obtained by applying the proposed optimization technique.