Developing a CFD heat transfer model for applying high expansion foam in an LNG spill

Liquefied natural gas (LNG) is widely used to cost-effectively store and transport natural gas. However, a spill of LNG can create a vapor cloud, which can potentially cause fire and explosion. High expansion (HEX) foam is recommended by the NFPA 11 to mitigate the vapor hazard and control LNG pool fire. In this study, the parameters that affect HEX foam performance were examined using lab-scale testing of foam temperature profile and computational fluid dynamics (CFD) modeling of heat transfer in vapor channels. A heat transfer model using ANSYS Fluent® was developed to estimate the minimum HEX foam height that allows the vapors from LNG spillage to disperse rapidly. We also performed a sensitivity analysis on the effect of the vaporization rate, the diameter of the vapor channel, and the heat transfer coefficient on the required minimum height of the HEX foam. It can be observed that at least 1.2 m of HEX foam in height are needed to achieve risk mitigation in a typical situation. The simulation results can be used not only for understanding the heat transfer mechanisms when applying HEX foam but also for suggesting to the LNG facility operator how much HEX foam they need for effective risk mitigation under different conditions.     

LES – DFSD modelling of vented hydrogen explosions in a small-scale combustion chamber

Accidental explosions are a plausible danger to the chemical process industries. In the event of a gas explosion, any obstacles placed within the path of the flame generate turbulence, which accelerates the transient flame and raises explosion overpressure, posing a safety hazard. This paper presents numerical studies using an in-house computational fluid dynamics (CFD) model for lean premixed hydrogen/air flame propagations with an equivalence ratio of 0.7. A laboratory-scale combustion chamber is used with repeated solid obstacles. The transient compressible large eddy simulation (LES) modelling technique combined with a dynamic flame surface density (DFSD) combustion model is used to carry out the numerical simulations in three-dimensional space. The study presented uses eight different baffle configurations with two solid obstructions, which have area blockage ratios of 0.24 and 0.5. The flame speed, maximum rate of pressure-rise as well as peak overpressure magnitude and timing are presented and discussed. Numerical results are validated against available published experimental data. It is concluded that, increasing the solid obstacle area blockage ratio and the number of consecutive baffles results in a raised maximum rate of pressure rise, higher peak explosion overpressure and faster flame propagation. Future model development would require more experimental data, probably in a more congested configuration.     

Analysis of the severity of vehicle-bicycle crashes with data mining techniques

Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes.     

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study

Introduction: Safety of pedestrians depends, among other factors, on their behavior while crossing the road. This study aims to assess behaviors of pedestrians at signalized crosswalks. Method: Following a literature review and a pilot study, 25 vital pedestrian crossing factors and behaviors were determined. Then data was randomly collected for 708 pedestrians at 10 lighted crossings in Sharjah (UAE), five at road intersections and five mid-block crossings. Results: Results indicated that 17.4% of pedestrians observed crossed partly or fully on red and that crossing speed was 1.22 m/s, on the average, which is slightly faster than most speeds recorded in the literature. Moreover, female pedestrians were more likely to cross while chatting with others, less likely to cross on red, and more likely to walk slower than male pedestrians. Results also showed that pedestrians who crossed at road intersections walked slower than those who crossed at mid-block crossings. It was also found that longer red pedestrian times and narrower roads tended to encourage pedestrians to cross on red and that the majority of pedestrians did not look around before crossing. Practical implications: Use of the Health Belief Model for pedestrian safety are discussed.     

高速公路货车事故成因及货车专用道分析

为减少货车对行车安全的影响,在充分考虑货车行驶理论、交通环境和驾驶员特征的基础上,就载重货车交通对高速公路运营安全的影响进行研究。其结果表明,不同车型之间的运行速度差异、超载、驾驶员不良交通行为及纵向间距不足等是造成货车交通事故的主要原因。通过分析货车事故与交通状况的关系,从工程技术可行性等角度提出了货车专用道的设计方法,以有效执行客货分行,提高高速公路运营安全水平。     

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes. Method: Analysis of Crashworthiness Data System’s (CDS) data show the head and thorax are the most common regions of impact of severe injuries, while the neck is the least. Irrespective of body regions, higher-rated vehicles were found to provide better occupant protection to both younger and older driver age groups. Assessment based on injury severity score (ISS) indicates that higher-rated vehicles have an overall lower average ISS compared to lower-rated vehicles. Results: Ultimately, this study shows that vehicles rated with National Highway Traffic Safety Administration’s (NHTSA) new criteria had lower average ISS compared to vehicles rated under the old criteria. The 2011 NHTSA side impact rating criteria being relatively new, it has very few crashes to draw meaningful statistically significant conclusions. However, this paper establishes the fact that vehicles with higher star ratings (under experimental conditions) indeed offer increased occupant protection in the field conditions. Practical applications: Previous studies have found that safety was given priority while buying new vehicles. However, people associated vehicle safety with technologies and specific safety features rather than the vehicle’s crash test results or ratings (Koppel, Charlton, Fildes, & Fitzharris, 2008). The results from this study provide a point of reference for safety advocates to educate the drivers about the importance of considering vehicle safety ratings during a vehicle purchase.     

Spatial relationships between alcohol outlet densities and drunk driving crashes: An empirical study of Tianjin in China

Introduction: Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes. Method: 3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables. Results: The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. The SDM is found to be the optimal spatial model to characterize the relationship between alcohol outlets and crashes. The number of alcohol-involved traffic crashes is positively related to population density and retail density, but negatively related to the company density, hotel density, and residential density within the same TAZ. Meanwhile, dense population and hotels have reverse spillover effects in adjacent zones. Conclusions: The significant spatial direct effect and spillover effect of alcohol outlet densities on drunk driving crashes should not be neglected. These findings could help improve transportation planning, traffic law enforcement and traffic management for large cities in developing countries.     

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.