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.     

Analysis of a regenerative braking system for Hybrid Electric Vehicles using an Electro-Mechanical Brake

The regenerative braking system of the Hybrid Electric Vehicle (HEV) is a key technology that can improve fuel efficiency by 20∼50%, depending on motor size. In the regenerative braking system, the electronically controlled brake subsystem that directs the braking forces into four wheels independently is indispensable. This technology is currently found in the Electronic Stability Program (ESP) and in Vehicle Dynamic Control (VDC). As braking technologies progress toward brake-by-wire systems, the development of Electro-Mechanical Brake (EMB) systems will be very important in the improvement of both fuel consumption and vehicle safety. This paper investigates the modeling and simulation of EMB systems for HEVs. The HEV powertrain was modeled to include the internal combustion engine, electric motor, battery and transmission. The performance simulation for the regenerative braking system of the HEV was performed using MATLAB/Simulink. The control performance of the EMB system was evaluated via the simulation of the regenerative braking of the HEV during various driving conditions.     

Belt routing and safety assessment through computer simulation

Most of the research on safety belt systems has involved crash simulation that only considered a dynamic human model. However, belt routing analysis, usually known as comfort level estimation, is also an important factor in safety belt design, considering that serious injuries of the abdominal region result from the infiltration of a belt into the neck or the chest. Thus, safety belt evaluations using kinematic human models are also needed. In this paper, a belt fit simulation method is suggested. Using the proposed process, both comfort and safety analyses can be performed under the same conditions continuously, and thus the safety belt design parameters, such as the location of anchor points, dummy posture and etc., can be evaluated. In conclusion, this computer process enables a belt system design to reduce injuries.     

Estimation of Tire-Road Friction Using Observer Based Identifiers

This paper presents methods for identifying the tire-road friction coefficient. The proposed methods are: an observer-based least square method and an observer/filtered-regressor-based method. These methods were designed assuming that some of the states are not available since physical parameter identification methods developed assuming that the system states are available are not attractive from a practical point of view. The observer is used to estimate signals which are difficult or expensive to measure. Using the estimated states of the system and the filtered-regressor, the parameter estimates are obtained. The proposed methods are evaluated on an eight state nonlinear vehicle/transmission simulation model with a Bakker-Pacejka's formula tire model. Vehicle tests have been performed on dry and wet roads to verify the performance of the methods. It has been shown through simulations and vehicle tests how the RPM sensors can be used with observer based identification methods to estimate the tire-road friction from measurements of engine rpm, transmission output speed and wheel speed. The proposed methods will be useful in the implementation and adaptation of vehicle collision warning/avoidance algorithm since the tire-road friction can be estimated only using the RPM sensors which are currently being used in production vehicles.     

Investigation on evaluation of yaw stability for medium commercial vehicles

This paper proposes test scenarios for evaluation of yaw stability for medium commercial vehicles. Maneuvering, speed, longitudinal tire force, tire-road friction coefficient, road slope, and load condition are considerable factors that have effect on the medium commercial vehicle yaw stability. After conducting an analysis on these six factors, effective test scenarios were developed. A sine with dwell test is well known as a test scenario for evaluation of performance of electronic stability control (ESC) on passenger vehicles and heavy commercial vehicles. The SWD test was modified considering medium commercial vehicle dynamics, and the ramp steer maneuver with maximum acceleration test was proposed. Simulation validation has been conducted using field test data. From simulation study, it was shown that the ESC system for medium commercial vehicle is effectively evaluated by the proposed test scenarios.     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

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.     

Application of a real-coded genetic algorithm for the fitting of a ship hull surface through a single non-uniform B-spline surface

In digital ship-design processes, surface modeling needs to be as accurate as possible for effectiveness in ship production as well as numerical analysis of the performance. Traditionally, the form of a ship hull is constructed from a set of cross-sectional data. This approach entails difficulties in the cross-sectional spacing and accuracy of the characteristic curves, such as the stern and bow profiles, deck side line, bottom tangential line, and unconnected curves. Genetic algorithms (GAs) have attracted increasing attention as a multimodal optimization solution for surface reconstruction that enable construction of a single non-uniform B-spline (NUB) surface at the initial stage of ship design with constraints such as knuckles, discontinuity conditions, and bulbous bows with high curvatures, . The first, simultaneous multi-fitting GA determines the boundary curves, such as the stem and stern profiles, and finds the common knot values for both curves. Similarly, the same GA technique is applied for other boundary curves at the bottom and the deck. The second GA is employed to fit the interior data points after the boundary curves are fitted. The encoded design variables for surface construction are the locations of the vertices and the knot values. Those variables are modified for improving the surface quality until a predefined degree of precision is attained. In four instances of application, the GA technique developed in this research has been shown to provide good, single, NUB surfaces with high efficiency. In the early design stage, a single NUB surface is more convenient for performance visualization and finite-element methods. It can be readily translated into many CAD/CAM packages, which facilitate the smooth transition of data across the different design stages.     

Optimal Design for the Rear-Glass Joint of an Automobile for Squeak and Rattle Noise Reduction

Ever since vehicle noise, vibration, and harshness (NVH) reduction technology made dramatic improvements, vehicle interior noises represented by Squeak and Rattle (S/R) becomes an ever more important factor to improve the emotional quality of vehicles. Generally, people detect S/R noises on automotive interior parts, brake system, suspension, Body in White (BIW), etc. Among them, the rear-glass joint is a major source for vehicle interior noise, and can cause S/R noises under a variety of environmental and driving conditions. This study uses, two approaches, experimental and numerical approaches, to define the cause of S/R noise at the rear-glass section. Based on these two approaches, this study confirms that S/R noises generate through the contact between bottom side of molding and BIW. The sealant penetration length, panelmolding distance, and sealant width are the parameters affecting noise generation. In addition, this study created an optimal design with Design of Experiments (DOE) of the rear-glass joint. The design maximized the sealant penetration length, which is a parameter that majorly affects noise. The optimal design comprises of two steps: sealant injections shape optimization and rear-glass joint parameter optimization. Each step is carried out with FEA and validated by sealant penetration experiments. Through these optimizations, this study obtained an optimum combination of design parameters and fignificantly reduced the noise generated by rear-glass section.     

A GIS‐based traffic analysis zone design: implementation and evaluation

The purpose of this paper is to implement an efficient method for GIS‐based traffic analysis zone (TAZ) design in order to evaluate and validate such a method. The method was developed by the authors.

Moran's I spatial autocorrelation coefficient and sample variance are used for evaluating the generated TAZs using the Champaign‐Urbana, IL region as a case study. Sensitivity analysis is also conducted to explore the fluctuations in TAZ generation outcomes. The evaluation, the validation as well as the TAZ design have been implemented with ARC/INFO GIS software on a UNIX workstation platform.