Path-following in model predictive rollover prevention using front steering and braking

In this paper vehicle path-following in the presence of rollover risk is investigated. Vehicles with high centre of mass are prone to roll instability. Untripped rollover risk is increased in high centre of gravity vehicles and high-friction road condition. Researches introduce strategies to handle the short-duration rollover condition. In these researches, however, trajectory tracking is affected and not thoroughly investigated. This paper puts stress on tracking error from rollover prevention. A lower level model predictive front steering controller is adopted to deal with rollover and tracking error as a priority sequence. A brake control is included in lower level controller which directly obeys an upper level controller (ULC) command. The ULC manages vehicle speed regarding primarily tracking error. Simulation results show that the proposed control framework maintains roll stability while tracking error is confined to predefined error limit.     

Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

     

Geometric stress distribution along the weld toe of the outer brace in two-planar tubular DKT-joints: Parametric study and deriving the SCF design equations

Regarding the research efforts expended so far on the calculation of stress concentration factors (SCFs) in tubular joints, two major shortcomings can be noted: (a) significant effort has been devoted to the study of SCFs in various uni-planar connections. Nevertheless, for multi-planar joints which cover the majority of practical applications, very few investigations have been reported due to the complexity and high cost involved; (b) majority of these research works focused on the study of SCFs at certain positions such as the saddle, crown toe, and crown heel, and they have ignored the hot-spot stress (HSS) at other positions along the weld toe. In the present paper, effects of dimensionless geometrical parameters on the SCF distribution along the weld toe of main (outer) braces in the axially loaded right-angle two-planar tubular DKT-joints are investigated. In order to study the multi-planar effect, SCF distribution in two-planar joints is compared with the distribution in a uni-planar joint having the same geometrical properties. A complete set of SCF database is constructed based on the two-planar DKT-joint Finite element models which are verified against experimental results and the predictions of Lloyd’s Register (LR) equations. The FE models cover a wide range of geometrical parameters. Six new SCF parametric formulae are developed through nonlinear regression analyses for the accurate and reliable fatigue design of two-planar DKT-joints under axial loads. An assessment study of these equations is conducted against the experimental data, the original FEM database and the acceptance criteria recommended by the UK Department of Energy.     

Reliability-based network flow estimation with day-to-day variation: A model validation on real large-scale urban networks

Day-to-day variation in the travel times of congested urban transportation networks is a frustrating phenomenon to the users of these networks. These users look pessimistically at the path travel times, and learn to spend additional time to safeguard against serious penalties that await late arrivals at the destinations. These additional expenses are charges similar to the tolls in system equilibrium flow problem, but may not be collected. With this conjecture, the user equilibrium (UE) formulation of congested network flow problem would lack some necessary factors in addressing appropriate path choices. This study, following a previous work proposing pessimistic UE (PUE) flow, aims to show how to measure this additional travel cost for a link, and investigates how different is PUE from UE, and when such differences are pronounced. Data are collected from the peak-hour travel times for the links of paths in the city of Tehran, to estimate the variance of travel times for typical links. Deterministic functions are obtained by calibrating the standard deviation of the daily variations of link travel times, and probabilistic functions by the technique of copula. UE and PUE traffic assignment models are built and applied to three large cities of Mashhad, Shiraz, and Tehran in Iran. The results show that the estimated flows by PUE model replicate the observed flows in screen lines much better than the UE model, particularly for longer trips. Since PUE is computationally equivalent to UE, this improvement is attained virtually at no cost.     

Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks

A method for determining optimal risk-based maintenance and rehabilitation (M&R) policies for transportation infrastructure is presented. The proposed policies guarantee a certain performance level across the network under a predefined level of risk. The long-term model is formulated in the Markov Decision Process framework with risk-averse actions and transitional probabilities describing the uncertainty in the deterioration process. The well known Conditional Value at Risk (CVaR) is used as the measure of risk. The steady-state risk-averse M&R policies are modeled assuming no budget restriction. To address the short-term resource allocation problem, two linear programming models are presented to generate network-level polices with different objectives. While the proposed methodology is general and can be used with any performance indicator, pavement roughness is used for numerical studies and an analytical expression for computing CVaR is derived.     

Flame propagation model for a rotary Atkinson cycle SI engine

The rotary Atkinson cycle engine includes two modes of combustion: combustion initiation and propagation in ignition chamber and then flame jet entrainment and propagation in expansion chamber. The turbulent flame propagation model is a predictive model for SI engines which could be developed for this type of combustion for the rotary Atkinson engine similar to the congenital engine with pre-chamber; in split combustion chamber SI engines, small amount of fuel is burned in pre-chamber while the fuel burned in ignition chamber of rotary Atkinson cycle is considerable. In this study a mathematical modeling of spherical flame propagation inside ignition chamber and new combined conical flame and spherical flame propagation model of a new two-stroke Atkinson cycle SI engine will be presented. The mathematical modeling is carried out using two-zone combustion analysis and the model also is validated against experimental tests and compared with previous study using non-predictive Weibe function model.     

Automated driving recognition technologies for adverse weather conditions

During automated driving in urban areas, decisions must be made while recognizing the surrounding environment using sensors such as camera, Light Detection and Ranging (LiDAR), millimeter-wave radar (MWR), and the global navigation satellite system (GNSS). The ability to drive under various environmental conditions is an important issue for automated driving on any road. In order to introduce the automated vehicles into the markets, the ability to evaluate various traffic conditions and navigate safely presents serious challenges. Another important challenge is the development of a robust recognition system can account for adverse weather conditions. Sun glare, rain, fog, and snow are adverse weather conditions that can occur in the driving environment. This paper summarizes research focused on automated driving technologies and discuss challenges to identifying adverse weather and other situations that make driving difficult, thus complicating the introduction of automated vehicles to the market.     

Mooring Damage Identification of Floating Wind Turbine Using a Non-Probabilistic Approach Under Different Environmental Conditions

This paper discusses the damage identification in the mooring line system of a floating wind turbine (FWT) exposed to various environmental loads. The proposed ...     

Telecommuting Suitability Modeling: An Approach Based on the Concept of Abstract job

A new approach to modeling telecommuting suitability is proposed in this paper. The approach, based on the concept of abstract job, can be employed to assess the level of suitability for telecommuting of the bundle of tasks comprising a job. By abstract job is meant a way of considering jobs on the basis of their elements and tasks, representing the general structure of the job. In this study, the basic tasks a job is composed of, pertaining to telecommuting suitability, are identified. To show the applicability of the approach, discrete choice models are calibrated, based on a sample of 245 employees in Tehran, Iran, indicating that from among the 6 tasks identified, 5 tasks are significantly associated with the level of telecommuting suitability.     

Investigation of pitch motion portion in vertical response at sides of a Tension-Leg Platform

Tendons vertically moor Tension-Leg Platforms (TLPs), thus, a deep understanding of physical tendon stresses requires the determination of the total axial deformation of the tendons, which is a combination of the heave, pitch, and surging responses. The vertical motion of the lateral sides of the TLP is coupled with surge and constitutes a portion of the pitch motion. Tendons are connected to the sides of the TLP; hence, the total displacement of the lateral sides is related to the total deformation of the tendons and the total axial stress. Therefore, investigating the total vertical response at the sides of the TLP is essential. The coupling between various degrees of freedom is not considered in the Response Amplitude Operator (RAO). Therefore, in frequency domain analysis, the estimated vertical RAO is incomplete. Also, in the time domain, only the heave motion at the center of TLP is typically studied; this problem needs to be addressed. In this paper, we investigate the portion of the pitch motion in the vertical response at the sides of the TLP in both the frequency and time domains. Numerical results demonstrate a significant effect of the pitch motion in the vertical motion of the edges of the TLP in some period ranges.