CFD validation studies for a high-speed foil-assisted semi-planing catamaran

This paper details the CFD validation studies carried out as a prerequisite for multi-fidelity CFD-based design optimization of high-speed passenger-only ferries aimed at reducing far-field wake energy that causes beach erosion. A potential flow program (WARP) and a URANS program (CFDSHIP) were validated using full-scale measurements of resistance, sinkage, trim, and far-field wake train obtained over a wide range of speeds for two high-speed semi-planing foil-assisted catamarans: Spirit (LOA-22 m) and 1060 (LOA-17 m). This study posed a unique combination of challenges for CFD modeling: the foil appended geometry required complicated surface overset grids, the effect of the waterjet and wind resistance had to be modeled, and a method had to be devised to extrapolate the calculated near-field elevation to get the far-field wake train using Havelock sources. A more concentrated effort was applied to the URANS verification and validation which forms the focus of this paper. The results show that URANS is able to accurately predict the resistance and motions for both vessels when coupled with models that account for the propulsors and air resistance. The overall accuracy of URANS for the performance analysis of the foil-assisted, semi-planing catamarans was adequate to warrant its use as a tool for subsequent design and optimization of a new vessel with significantly reduced wakes.     

A proposal of reliability-based design formats for ultimate hull girder strength checks for bulk carriers under combined global and local loadings

The purpose of this paper is to provide a basis for the development of reliability-based design formats for ultimate hull girder strength checks for bulk carriers in hogging conditions under combined global and local loading and to estimate implied safety levels in current rule practices for hull girders. The effect of alternative definitions of characteristic still-water loads on the safety format and, hence, the safety factors is assessed. The effect of systematic (bias) model uncertainties associated with loads and strength on the reliability measures is investigated.     

Modelling commuting mode choice with explicit consideration of carpool in the choice set formation

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).     

Behavioural responses to vehicle emissions charging

Road pricing as an economic construct is not a new phenomenon in transportation research. Whilst fuel taxation and tolling of roads are common ways of raising revenue in many countries, these initiatives are primarily aimed at road infrastructure financing. Worldwide there has been growing interest in pricing structures designed to also manage the growing levels of traffic congestion and, in recent times, an increasing focus on generating reductions in carbon emissions from vehicle ownership and use. This paper presents a stated choice experiment undertaken in response to the increasing interest in the environmental externalities of travel behaviour. The aim of this choice experiment is to identify the potential to switch to more fuel efficient cars that emit lower emissions, under differing pricing and technology scenarios. Results indicate that annual and variable emissions surcharges targeted very specifically to vehicle emission rates have a noticeable role in modifying vehicle purchasing decisions.     

Large-scale road network vulnerability analysis: a sensitivity analysis based approach

Traditionally, an assessment of transport network vulnerability is a computationally intensive operation. This article proposes a sensitivity analysis-based approach to improve computational efficiency and allow for large-scale applications of road network vulnerability analysis. Various vulnerability measures can be used with the proposed method. For illustrative purposes, this article adopts the relative accessibility index (AI), which follows the Hansen integral index, as the network vulnerability measure for evaluating the socio-economic effects of link (or road segment) capacity degradation or closure. Critical links are ranked according to the differences in the AIs between normal and degraded networks. The proposed method only requires a single computation of the network equilibrium problem. The proposed technique significantly reduces computational burden and memory storage requirements compared with the traditional approach. The road networks of the Sioux Falls city and the Bangkok metropolitan area are used to demonstrate the applicability and efficiency of the proposed method. Network manager(s) or transport planner(s) can use this approach as a decision support tool for identifying critical links in road networks. By improving these critical links or constructing new bypass roads (or parallel paths) to increase capacity redundancy, the overall vulnerability of the networks can be reduced.     

Computational methodology to predict injury risk for motor vehicle crash victims: A framework for improving Advanced Automatic Crash Notification systems

Advanced Automatic Crash Notification (AACN) systems, capable of predicting post-crash injury severity and subsequent automatic transfer of injury assessment data to emergency medical services, may significantly improve the timeliness, appropriateness, and efficacy of care provided. The estimation of injury severity based on statistical field data, as incorporated in current AACN systems, lack specificity and accuracy to identify the risk of life-threatening conditions. To enhance the existing AACN framework, the goal of the current study was to develop a computational methodology to predict risk of injury in specific body regions based on specific characteristics of the crash, occupant and vehicle. The computational technique involved multibody models of the vehicle and the occupant to simulate the case-specific occupant dynamics and subsequently predict the injury risk using established physical metrics. To demonstrate the computational-based injury prediction methodology, three frontal crash cases involving adult drivers in passenger cars were extracted from the US National Automotive Sampling System Crashworthiness Data System. The representative vehicle model, anthropometrically scaled model of the occupant and kinematic information related to the crash cases, selected at different severities, were used for the blinded verification of injury risk estimations in five different body regions. When compared to existing statistical algorithms, the current computational methodology is a significant improvement toward post-crash injury prediction specifically tailored to individual attributes of the crash. Variations in the initial posture of the driver, analyzed as a pre-crash variable, were shown to have a significant effect on the injury risk.     

Identification of occupant posture using a Bayesian classification methodology to reduce the risk of injury in a collision

Recent studies have shown that smart restraint systems, which will recognize and then adapt to a specific collision and occupant combination, have a strong opportunity to significantly reduce occupant injuries during a traffic accident. As a step toward the development of these adaptive restraint systems, this study proposes a novel methodology for the classification of pre-crash occupant posture. Various occupant postures were simulated with a human model and the corresponding data was recorded using sensor models implemented in a mid-size car interior. The sensor data was then used to train two Bayesian classifiers which categorized an unknown occupant posture as one of nine predefined classes. The posture classifiers and a look-up table which contained optimized restraint laws for each class were combined to form catalog controllers for the restraint systems. The benefit of these restraint systems with catalog controllers vs. a restraint system optimized at a nominal posture was estimated by analyzing crash simulations with the occupant in 200 different postures. While the minimum error rate classifier showed the highest correct classification rate (90%), the Bayesian minimum risk classifier estimated the highest average injury reduction (21%). As expected, the highest injury reduction (up to 45%) was recorded for the posture classes closest to the windshield, whereas the lowest injury reduction was found for the classes closest to the nominal position. While the proposed restraint system with a catalog controller requires considerable “offline” computational effort, it is more versatile in terms of using complex human models and injury criteria and is much faster during the brief decision window available than recent “online” controllers proposed previously in literature.     

Walking frequency, cars, dogs, and the built environment

To explain walking propensity or frequency, empirical studies have generally used two sets of explanatory variables, namely, socio-demographic variables and built environment variables. They have generally shown that both socio-demographic characteristics and built environment characteristics are associated with walking propensity. We examine the traditional walkability variables that encompass density, mix of uses, and network connectivity in New Jersey, using a statewide sample including an oversample of Jersey City. We estimate a two-stage least squares model using a conditional mixed process that combines an ordered probit model of walking frequency in the second stage based on a truncated regression of car ownership in the first stage. Our results show that built environment variables have some small effects, mainly from better network connectivity associated with increased walking frequency. One of our key findings is that built environment features also work indirectly via how they influence car ownership. In general, we find sufficient evidence that suggests fewer cars are owned in areas with more walkable built environment features. The other key variable that we control for is whether a household owns a dog. This also proved to be strongly associated with walking suggesting that dog ownership is a necessary control variable to understand the frequency of walking.     

大型加强板结构焊接顺序的效果研究(英文)

Welding sequence has a significant effect on distortion pattern of large orthogonally stiffened panels normally used in ships and offshore structures. These deformations adversely affect the subsequent fitup and alignment of the adjacent panels. It may also result in loss of structural integrity. These panels primarily suffer from angular and buckling distortions. The extent of distortion depends on several parameters such as welding speed, plate thickness, welding current, voltage, restraints applied to the job while welding, thermal history as well as sequence of welding. Numerical modeling of welding and experimental validation of the FE model has been carried out for estimation of thermal history and resulting distortions. In the present work an FE model has been developed for studying the effect of welding sequence on the distortion pattern and its magnitude in fabrication of orthogonally stiffened plate panels.     

Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

Studies that link human behaviour to the influence of weather have historically been conducted in such fields as tourism, marketing and leisure. In most studies that jointly examine weather and the mode of transport, only open-air transportation has been considered (for example, bicycle, motorcycle or walking). This focus, together with the habitual use of data collected with automatic devices and a lack of studies that analyse this issue using stated preference data, are the main reasons motivating this paper. This paper aims to analyse the influence of weather and the density of traffic on the choice of transport mode. A case study is conducted in an access/egress corridor located in the city of Barcelona (Spain). Two data sources were used: revealed preference and stated preference data. Modelling techniques using mixed data enabled the stronger features from both data sources to be captured. Finally, we discuss how the selection of different alternative specific constants in models estimated using mixed data could generate unrealistic forecasting results if environmental changes are expected in the actual market.