Quantitative assessment of hydrocarbon explosion and fire risks in offshore installations

A risk-based design framework should involve both risk assessment and risk management. This article introduces and describes a number of procedures for the quantitative assessment and management of fire and gas explosion risks in offshore installations. These procedures were developed in a joint industry project on the explosion and fire engineering of floating, production, storage and off-loading units (the EFEF JIP), which was led by the authors. The present article reports partial results, focussing on defining the frequency of fires and explosions in offshore installations. Examples of the aforementioned procedures’ application to a hypothetical floating, production, storage, and off-loading unit (FPSO) are presented. A framework for the quantitative risk assessment of fires and explosions requires the definition of both the frequency and consequences of such events. These procedures can be efficiently applied in offshore development projects, and the application includes the assessment of design explosion and fire loads as well as the quantification of effects of risk control options (RCO) such as platform layout, location and number of gas detectors, isolation of ignition sources etc.     

Comparative study on model-scale sloshing tests

This paper considers a comparative study on model-scale sloshing tests. There are two primary scopes of this study: the comparison of sloshing pressure measured in 1/50-scale model tests at Seoul National University (SNU) with (1) the data measured at the other facility for the same model, and (2) the data measured on a smaller scale model. For the comparative study, model tanks are excited with the same irregular motions with Froude scale, and sloshing pressure signals are measured at the same locations. The statistical quantities of 1/50-scale model tests are compared with those of other facility and 1/70-scale model tests. In this study, it is found that peak pressure measured at SNU are slightly lower than those of other facility, and this difference may be due to different sensor types and sensing diameters.     

Development of a control strategy based on the transmission efficiency with mechanical loss for a dual mode power split-type hybrid electric vehicle

In this study, a control strategy for a dual mode power split-type hybrid electric vehicle (HEV) is developed based on the powertrain efficiency. To evaluate the transmission characteristics of the dual mode power split transmission (PST), a mechanical loss model of the transmission (TM loss) is constructed. The transmission efficiency, including the TM loss, is evaluated for the dual mode PST. Two control strategies for the dual mode PST are proposed. An optimal operation line (OOL) control strategy is developed to maintain a high engine thermal efficiency by controlling the engine operation point on the OOL. A speed ratio (SR) control strategy is proposed to obtain a greater transmission efficiency by shifting the engine operation point when the dual mode PST operates near the mechanical points. Using the TM loss and the proposed control strategies, a vehicle performance simulation is conducted to evaluate the performance of the two control strategies for dual mode PST. The simulation results demonstrate that, for the SR control strategy, the engine efficiency decreases because the engine operates beyond the OOL. However, the transmission efficiency of the dual mode PST increases because the PST operates near the mechanical point where the PST shows the greatest transmission efficiency. Consequently, the fuel economy of the SR control strategy is improved by 3.8% compared with the OOL control strategy.     

Robust design optimization of suspension system by using target cascading method

This study presents the robust design optimization process of suspension system for improving vehicle dynamic performance (ride comfort, handling stability). The proposed design method is so called target cascading method where the design target of the system is cascaded from a vehicle level to a suspension system level. To formalize the proposed method in the view of design process, the design problem structure of suspension system is defined as a (hierarchical) multilevel design optimization, and the design problem for each level is solved using the robust design optimization technique based on a meta-model. Then, In order to verify the proposed design concept, it designed suspension system. For the vehicle level, 44 random variables with 3% of coefficient of variance (COV) were selected and the proposed design process solved the problem by using only 88 exact analyses that included 49 analyses for the initial meta-model and 39 analyses for SAO. For the suspension level, 54 random variables with 10% of COV were selected and the optimal designs solved the problem by using only 168 exact analyses for the front suspension system. Furthermore, 73 random variables with 10% of COV were selected and optimal designs solved the problem by using only 252 exact analyses for the rear suspension system. In order to compare the vehicle dynamic performance between the optimal design model and the initial design model, the ride comfort and the handling stability was analyzed and found to be improved by 16% and by 37%, respectively. This result proves that the suggested design method of suspension system is effective and systematic.     

Improved hot-stamping analysis of tubular boron steel with direct measurement of heat convection coefficient

There are two types of hot-stamping processes, direct and indirect, depending on the sequence of the heating, forming, and quenching steps and the method used for each step. In this study, an indirect hot-stamping process consisting of forming at room temperature, heating, and water quenching was applied to develop a coupled torsion beam axle. The analysis results indicated that the application of the heat convection coefficient is critical in the simulations and must take into account the temperature and specific location in the model to ensure the accuracy of the heating and quenching analysis. The heat convection coefficients used in the analysis were directly measured at various positions of the tube (e.g., outside, inside, and bending region) using thermocouples, and the final values were determined through correlation between the actual tests and numerical analysis. The experimental and simulated final deformed shape and temperature distribution were in good agreement.     

Age and cross-cultural comparison of drivers’ cognitive workload and performance in simulated urban driving

Driving demands significant psychomotor attention and requires even more when drivers are engaged in secondary tasks that increase cognitive workload and divert attention. It is well established that age influences driving risk. Less is known about how culture impacts changes in attention. We conducted parallel driving simulations in the US and Korea to measure the extent to which age and culture influence dual-task performance. There were 135 participants divided into two groups: a younger group aged 20∼29, and an older group aged 60∼69. Whereas some differences by culture appeared in absolute control measures, the younger participants showed similar mean velocity and compensatory patterns associated with increased cognitive load in the urban setting; however, the results from the older samples were less similar.     

Stochastic analysis of the variation in injury numbers of automobile frontal crash tests

Although automobile crash test data have a comparatively large variation because of the complexity of the tests, only a limited number of crash tests are usually conducted due to monetary and time limitations. Thus, it is necessary to control input variables that cause the variation in test data to obtain consistent crash test results and to correctly assess the safety performance of an automobile under development. In this study, a MADYMO model was validated deterministically to yield the head, chest, pelvis deceleration pulses of anthropomorphic test devices and the belt load pulses similar to those from actual tests, and it was also validated stochastically to yield means and standard deviations of the head and chest injury numbers, i.e., HIC ₁₅ and 3 msec clip similar to those from actual tests. A stochastic analysis was conducted using the validated MADYMO model to calculate the sensitivity of the standard deviations of the injury numbers to the standard deviations of influential input variables to determine the most influential input variable that makes the largest contribution to the variation in the injury numbers. Moreover, the Taguchi approach was used to determine the optimal values of the influential input variables to improve safety performance.     

Traffic-balancing algorithm for can systems with dual communication channels to enhance the network capacity

The controller area network (CAN) is the dominant protocol for in-vehicle network (IVN) systems because it provides bounded transmission delay among electronic control units (ECUs) at data rates between 100 Kbps and 1 Mbps. Many automotive companies have chosen the CAN protocol for their chassis network system of intelligent vehicles. However, the increasing number of ECUs in intelligent vehicles and the need for more intelligent functions require a network system with more network capacity and real-time capability. As one approach to enhance the network capacity of a CAN system, this paper introduces a CAN system with dual communication channels. This paper also presents a traffic-balancing algorithm that predicts the traffic of each channel and allocates frames to the most appropriate channel. An experimental testbed using commercial off-the-shelf microcontrollers with two CAN controllers was used to demonstrate the feasibility of the traffic-balancing algorithm.     

Lane assignment problem using a genetic algorithm in the Automated Highway Systems

This paper deals with the lane assignment problem in the Automated Highway Systems (AHS). The purpose of lane assignment strategies is to increase throughput on multi-lane highways with many entry and exit points, based on the itineraries of vehicles using the highway (point of entry, time of entry and exit point) and the effects of maneuvers. Using partitioned lane assignment strategies, the lane assignment problem is formulated as an optimization problem to find the proper positions of partitions. Then the optimal problem is solved using a Genetic Algorithm (GA). The proposed algorithm has been validated by comparing its solution with that of an all-cases algorithm for a simple case. Moreover, a traffic simulator with a realistic car-following model as well as lane change logic has been developed. The simulation results show the efficiency of the proposed genetic algorithm, compared with random assignment algorithms.