Torsion beam axle system design with a multidisciplinary approach

This research proposes an automatic torsion beam axle optimization process with a multidisciplinary approach and generates the optimal torsion design parameters, such as thickness and shape. In order to construct an automatic analysis process, multidisciplinary analysis models, such as modal analysis, roll mode dynamic analysis, and fatigue analysis, were applied in batch mode. To understand the design space, a parametric study using the torsion beam thickness and shape was performed. Considering roll durability and K&C characteristics, the torsion beam axle could be optimized. For the automated design process, a PIDO tool called PIAnO was used. In conclusion, a reduction in the computer-aided simulation time was achieved, and the durability and K&C characteristics of the torsion beam were enhanced by optimizing the thickness and shape.     

Development of a knowledge-based hybrid failure diagnosis system for urban transit

Urban transit is a complex system that contains both electrical and mechanical entities; therefore, it is necessary to construct a maintenance system for ensuring safety during high-speed driving. Expert systems are computer programs that use numerical or non-numerical domain-specific knowledge to solve problems. This research aims to develop an expert system that diagnoses the causes of failures quickly and displays measures to correct them. For the development of this expert system, the standardization of a failure code classification and the creation of a Bill of Materials (BOM) were first performed. Through the analysis of both failure history and maintenance manuals, a knowledge base has been constructed. Also, for retrieving the procedure of failure diagnosis and repair linking with the knowledge base, we have built a Rule-Based Reasoning (RRB) engine with a pattern matching technique and a Case-Based Reasoning (CBR) engine with a similar search method. Finally, this system has been developed as web based in order to maximize accessibility.     

Design and validation of a longitudinal velocity and distance controller via hardware-in-the-loop simulation

In this paper, a set of longitudinal velocity and distance controllers with switching logic is proposed for an active driver safety system, and validation via hardware-in-the-loop simulation (HILS) is presented. Since the desired velocity and distance are given discretely and arbitrarily by a driver, there are usually discontinuities or discrete jumps between the desired and current vehicle state immediately after the switching. To minimize performance degradation resulting from this discrete jump, dynamic surface control (DSC) with an input-shaping filter is applied for both velocity and distance control. Furthermore, while much cost and effort are usually necessary for the experimental validation of a longitudinal controller, the validation of the longitudinal controller via HILS is performed with a minimum of effort. In the HILS, the various switching scenarios and desired discrete inputs in terms of velocity and distance are considered and the corresponding performance of the controller is shown in the end.     

Development of quantitative measuring technique to find critical flow conditions for preventing soot deposit accumulated in the diesel exhaust system using main muffler composed of three chambers

If a vehicle that meets emission regulations operates sufficiently for a long time under low speed and low load conditions, soot contained in the exhaust gas is accumulated on the inner surface of the exhaust system. This soot deposition problem occurs frequently in all diesel cars. However, when a vehicle is placed under the conditions of sudden start and sudden acceleration after city mode driving for a long time, the deposited soot is abruptly blown up with the soot produced during fuel combustion. In the present study, the main cause of the abrupt outburst of deposited soot is investigated to overcome this adverse phenomenon. First, we developed a method to quantify the amount of the exhausted soot particles (or the accumulated soot particles) by measuring the opacity that represents the contamination level of the exhaust gas due to soot particles. Using this measuring scheme for deposited soot, we found the critical conditions for engine speeds and load conditions at which soot particles are emitted into the air without accumulation in the exhaust system using main muffler composed of three chambers. In order to meet these critical conditions and thus to drastically reduce soot accumulation, the exhaust system using the main muffler applied in this study must be designed to ensure that the flow velocity of the exhaust gas is higher than 62 m/s when the back pressure at the exit of the turbocharger is under 0.08 bars.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

北美铁路经历的车轴故障

对北美铁路重载货车的车轴故障、车轴疲劳试验和车轴应力进行了研究,并介绍了几种解决车轴故障的措施.     

Air holding problem solving with reinforcement learning to reduce airspace congestion

The Air Holding Problem Module is proposed as a decision support system to help air traffic controllers in their daily air traffic flow management. This system is developed using an Artificial Intelligence technique known as multiagent systems to organize and optimize the solutions for controllers to handle traffic flow in Brazilian airspace. In this research, the air holding problem is modeled with reinforcement learning, and a solution is proposed and applied in two case studies of the Brazilian airspace. The system can suggest more precise and realistic actions based upon past situations and knowledge of the professionals and forecast the impact of restrictive measures at the local and/or overall level. The first case study shows performance improvements in traffic flows between 8 and 47% at the local level up to 49% at the overall level. In the second case study, performance improvements were between 15 and 57% at the local level and between 41 and 48% at the overall level. Copyright © 2014 John Wiley & Sons, Ltd.     

Nature of influence of out‐of‐vehicle time‐related attributes on transit attractiveness: a random parameters logit model analysis

This paper describes the nature of the impacts of walking distances and waiting time on transit use. The relative trade‐offs of walking and transfer components with other transit service attributes are also discussed. A total of 449 completed stated‐preference interviews were collected; with six observations from each respondent, the total number of observations was 2694. This data set was used to estimate the coefficients in different utility functions using a random parameters logit model. The results demonstrated that walking distances to and from transit stops have important and significant nonlinear negative influences on the attractiveness of transit. Transfer waiting time was also shown to have a significant nonlinear negative impact on transit attractiveness. The random parameters logit model had a better model fit than the standard logit model. Some of the findings obtained here are novel, while others are consistent with previous works. These findings have implications for both theory and practice. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimating probability distributions of dynamic queues

Queues are often associated with uncertainty or unreliability, which can arise from chance or climatic events, phase changes in system behaviour, or inherent randomness. Knowing the probability distribution of the number of customers in a queue is important for estimating the risk of stress or disruption to routine services and upstream blocking, potentially leading to exceeding critical limits, gridlock or incidents. The present paper focuses on time-varying queues produced by transient oversaturation during demand peaks where there is randomness in arrivals and service. The objective is to present practical methods for estimating a probability distribution from knowledge of the mean, variance and utilisation (degree of saturation) of a queue available from computationally efficient, if approximate, time-dependent calculation. This is made possible by a novel expression for time-dependent queue variance. The queue processes considered are those commonly used to represent isolated priority (M/M/1) and signal-like (M/D/1) systems, plus some statistical variations within the common Pollaczek-Khinchin framework. Results are verified by comparison with Markov simulation based on recurrence relations.     

Bayesian committee of neural networks to predict travel times with confidence intervals

Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy.