不同外加剂对OGFC混合料路用性能的影响

OGFC为开级配沥青混合料,主要由粗集料嵌挤组成,在世界各地越来越广泛地用于铺筑路面.OGFC混合料可以增大路表面渗透性,减少噪音,增强表面摩擦力,尤其是在潮湿条件下.该文主要研究不同外加剂对OGFC路用性能的影响,其混合料分别由再生聚乙烯改性沥青(RPEB)、橡胶粉改性沥青(CRMB)和70#基质沥青+纤维与矿料拌和而成.并分别采用析漏试验、肯塔堡飞散试验、渗水试验、劈裂试验、静态模量试验,车辙试验和抗滑试验评估OGFC混合料的路用性能.结果表明:纤维和聚合物可以有效地减小OGFC离析的可能性,混合料每面击实50次能相应地提高磨耗性和其他相关的性能.改性沥青混合料的抗拉强度比(TSR)低于纤维+基质沥青混合料的抗拉强度比.     

Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.     

Computational and experimental investigation of the unsteady flow structures around automotive outside rear-view mirrors

The unsteady flow field around two automotive outside rear-view mirrors is investigated. This study includes comprehensive experimental and computational approaches in order to characterize the complex flow structures in the wake of the mirrors. The experiments were carried out in a wind tunnel which included the measurements of the instantaneous and averaged velocity fields as well as mean and unsteady surface pressure distributions. The simulations were performed using Large Eddy Simulation (LES). The LES approach (particularly with the dynamic subgrid viscosity model) provided good agreements with the experiments for the velocity and the surface pressure distributions. The experimental and the computational results of this study will be used as a benchmark to validate the current and the future CFD development and the subsequent aero-acoustic computations.     

A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications

This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO₂ relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO₂ emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.     

Dual extended Kalman filter for vehicle state and parameter estimation

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.     

Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

In the case of full vehicle models, the technique of multi-body simulation (MBS) is frequently used to study their highly non-linear dynamic behaviour. Many non-linearities in vehicle models are induced by force elements like springs, shock absorbers, bushings and tires. Commonly, spline functions are used to represent the force responses of these components. If the non-linear relationships are more complicated, the spline approximations are no more accurate. An alternative approach is based on empirical neural networks which are based on the mathematical approximation of measured data. It is well known that neural networks are able to represent and predict complex component responses accurately. The aim of this paper is to perform a dynamic full vehicle simulation using a thermomechanically coupled hybrid neural network shock absorber model. In this shock absorber model, the spline approach is combined with a temperature-dependent neural network. Based on a displacement-controlled excitation on a four post test rig in the ADAMS/Car MBS software, a rugged test track is simulated. In this way, the front and rear shock absorbers are dynamically loaded with comfort-relevant frequencies in the range of 0.75-30 Hz and velocity amplitudes up to 2 m/s. By the simulation, stability of the hybrid neural network model is demonstrated. Furthermore, the damping force, the vertical acceleration of the chassis and the required simulation times are compared. The standard spline approach is used as a reference.     

Identification of potential sites for deep-ocean waste isolation with a geographic site-selection model

Identification of optimal sites for the isolation of waste on the abyssal seafloor was performed with two approaches: by the traditional method of map overlays of relevant attributes, and by a specially developed, automated Site-Selection Model (SSM). Five initial, Surrogate Sites, identified with the map-overlay approach, were then compared with the more rigorously produced scores from the SSM. The SSM, a process for optimization of site locations, accepts subjective, expert-based judgments and transforms them into a quantitative, reproducible, and documented product. The SSM is adaptable to any siting scenario. Forty-one factors relevant to the isolation scenario, including 21 weightable factors having a total of 123 scorable categories, have been entered into the SSM. Factors are grouped under project definition, unique environments, anthropogenic, geologic, biologic, weather, oceanographic and distance criteria. The factor scores are linked to a georeferenced database array of all factors, corresponding to 1°×1° latitude–longitude squares. The SSM includes a total of 2241 one-degree squares within 1000 n.m. of the U.S. coasts, including the western North Atlantic, the Gulf of Mexico, and the eastern North Pacific. Under a carefully weighted and scored scenario of isolation, the most favorable sites identified with the SSM are on the Hatteras and Nares Abyssal Plains in the Atlantic. High-scoring sites are also located in the Pacific abyssal hills province between the Murray and Molokai Fracture Zones. Acceptable 1° squares in the Gulf of Mexico are few and of lower quality, with the optimum location on the northern Sigsbee Abyssal Plain. Two of the five Surrogate Site locations, on the Hatteras and Sigsbee Abyssal Plains, correspond to the best SSM sites in each ocean area. Two Pacific and a second Atlantic Surrogate Site are located in low-scoring regions or excluded by the SSM. Site-selection results from the SSM, although robust, are an initial attempt to quantify the site-selection process. The SSM database exposes a significant lack of high-quality information for many areally mappable attributes on the abyssal seafloor, particularly bottom-current speed and measures of biologic productivity and flux. Terminologies and classifications of some measures, such as sediment types, suffer from parochialism and vary by ocean. Considerable research is needed even for a broad understanding of the environmental measures required to make sound societal decisions about use of the abyssal seafloor for disposal or other purposes.