Abstract

This study presents the results from detailed experiments of the two-phase pressurized flow behavior during the rapid filling of a large-scale pipeline. The physical scale of this experiment is close to the practical situation in many industrial plants. Pressure transducers, water-level meters, thermometers, void fraction meters, and flow meters were used to measure the two-phase unsteady flow dynamics. The main focus is on the water–air interface evolution during filling and the overall behavior of the lengthening water column. It is observed that the leading liquid front does not entirely fill the pipe cross section; flow stratification and mixing occurs. Although flow regime transition is a rather complex phenomenon, certain features of the observed transition pattern are explained qualitatively and quantitatively. The water flow during the entire filling behaves as a rigid column as the open empty pipe in front of the water column provides sufficient room for the water column to occupy without invoking air compressibility effects. As a preliminary evaluation of how these large-scale experiments can feed into improving mathematical modeling of rapid pipe filling, a comparison with a typical one-dimensional rigid-column model is made. Keywords: Experimentation, Large-scale pipeline, Unsteady flow, Two-phase flow, Air–water interface, Flow-regime transition

Abstract

CO2 flue gases acquired from different sources contain a significant amount of impurities and water, which are corrosive to the pipeline steel. To design the pipelines for large scale of CO2 flue gas transport, the corrosion of pipeline steels has to be investigated in the realistic conditions. In this paper, corrosion behaviour of steel S355 and stainless steel 316L in CO2 flue gas solutions has been investigated using electrochemical techniques in an autoclave. The corrosion rates of the steels in different environments were measured by polarization and mass loss measurements. The corrosion morphology and products on the steel surface were analyzed by photo- And electron microscope and x-ray diffraction, respectively. Three types of polymer coatings on the steel S355 have been studied in 3.5% NaCl solution plus CO2 in the autoclave at 100 bar and 60°C. The corrosion resistance of the coatings has been measured by electrochemical impedance measurements. The structures and the adhesion of the coatings before and after the autoclave tests have been investigated using microscope and pull-off test. The results show that the Coating B has better corrosion resistance than the other two types of coatings, which is a good candidate for internal coatings of a pipeline. © 2013 The Author.

Abstract

This paper presents an integrated vehicle model to simulate simultaneously the driver, powertrains, chassis, body, road condition, vehicle dynamics and the Active Suspension (AS) system with/without an energy harvesting module. The developed model is used to investigate the ride comfort and influences of energy harvesting AS system on the total energy consumption of battery Electric Vehicles (EVs) relative to EVs with a passive suspension system. Preliminary simulation results show that compared to EVs with a passive suspension system, the ones with AS system improve ride comfort, up to 31% reduction of the vehicle body acceleration RMS value, with an expense of higher energy consumption. This expense can be reduced to about 2.8% when using an energy harvesting AS system. Simulation results also demonstrate that the available energy for recuperation during the AS system operation is significant in relation to the regenerative braking energy of the propulsion system, up to approx. 70% on bumpy road surfaces.

Abstract

More and more Advanced Driver Assistance Systems (ADASs) are entering the market for improving both safety and comfort. Adaptive Cruise Control (ACC) is an ADAS application that has high interaction with the driver. ACC systems use limited sensor input and have only few configuration possibilities. This may result in the behaviour of the ACC not matching user's preferences in all cases, resulting in lower acceptance of the system. In this work, we examine the possibilities for a Personalised ACC (PACC), which adapts the ACC settings such that it matches the driver preference in order to increase the acceptance. The driver preferred ACC behaviour is predicted using machine learning techniques and manual driving data. On-road experiments showed that the method is promising as it is able to discriminate between two preference clusters with an accuracy of 85%.

Abstract

For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging strategy is developed to minimize the energy cost. This is done by exploiting the periodicity and predictable operation of the city buses. For effective usage of the developed offline strategy, the actual energy demand of the electric bus must be known a-priori, which can be demanding. In order to address this issue, a predictor is designed. The neural network based predictor is able to estimate the energy demand for the next day. Using this, three different optimal charging strategies are implemented. Initially, only the operational constraints are considered to ensure the completion of a trip, later, a more involved problems consisting of battery state of charge (SoC) constraints and temperature constraints are included for the second and third optimization problems, respectively. All the three approaches result in significant energy cost minimization when compared to the non-optimal strategy of charging the electric bus to the full capacity at every available opportunity. Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.

Abstract

The SPECIALIST algorithm can resolve jam waves on freeways using roadside technology: detector loops and speed limit gantries. In this paper we extend the algorithm, enabling the integration with cooperative system technologies and other road side detectors, such as in-car detection and actuation, and video-based monitoring (VBM). Integrating these cooperative elements can provide faster and more accurate jam detection, which leads to a better performance of the SPECIALIST algorithm. For the fusion of the various data sources (loops, VBM, floating car data) an extension of the Adaptive Smoothing Method is used. The data fusion method is also adapted to comply with the input requirements of the SPECIALIST algorithm. The resulting system is suitable for a mixed roadside/in-car detection and actuation environment. The performance of the resulting system is evaluated using the microscopic simulator VISSIM. The results show that floating car data and VBM can considerably improve the jam detection times and the accuracy of the detected jam location, which lead to more efficient speed limit schemes.

Abstract

This paper addresses technical and operational aspects pertaining to the transport of CO2. It deals with lessons learnt from the development of three large CCS demonstration projects: the UK-based Don Valley project, the Dutch ROAD project, and the Spanish Compostilla project. These projects were all selected by the European Commission in 2009 to receive funding under the European Energy Programme for Recovery (EEPR). The purpose of the demonstration projects is to verify feasible capture techniques (i.e. gasification, flue-gas cleaning, and oxy-coal combustion in circulating fluidised bed, respectively), and to demonstrate geological storage options, off-shore and on-shore. As the distance and elevation of the CO2 transport system are inherently given by the project, the transport conditions for the CO2 will generally differ from one project to another.The demonstration projects have shown that the thermophysical nature of CO2 is prone to complicate certain operational procedures mainly due to phenomena like phase change, hydrate formation and Joule-Thomson cooling. The front-end engineering design studies suggest, however, that the handling of CO2 is quite feasible during normal operation, although customised solutions may be required to handle transients like emergency shut-down and pipeline re-pressurisation. This implies that CO2 transport is not seen as an insuperable hurdle to the design and operation of large CCS systems.

Abstract

More and more Advanced Driver Assistance Systems (ADAS) are entering the market for improving both safety and comfort by assisting the driver with their driving task. An important aspect in developing future ADAS and Automated Driving Systems (ADS) is testing and validation. Validating the failure rate of an ADS requires so many operational hours that testing in real time is almost impossible. One way to reduce this test load is virtual testing or hardware in-The-loop testing. The major challenge is to create realistic test cases that closely resemble the situation on the road. We present a way to use data of naturalistic driving to generate test cases for Monte-Carlo simulations of ADS. Because real-life data is used, the assessment allows to draw conclusions on how the ADS would perform in real traffic. The method, developed in EU AdaptIVe, is demonstrated by testing an Adaptive Cruise Control (ACC) system in scenarios where the predecessor of the ego vehicle is braking. We show that the probability of the occurrence of unsafe situations with the ACC system can be accurately and efficiently determined.

Abstract

This paper presents the results of the assessment phase of the project PRIMA (Pro-Active Incident Management), where the benefits, costs and risks of novel traffic incident management techniques are investigated. The project targets the enhancement of current state-of-the-art measures for handling incidents, i.e. road accidents, breakdowns and congestion, by recommending proactive techniques. To this end, modern technologies such as eCall, C2X or xFCD are assessed in terms of their potential to optimize the overall incident duration and response. Results are derived from a scenario-based assessment methodology involving macroscopic traffic simulations, traffic performance calculations and cost-benefit analysis. The work presented in this paper is being followed up by the development of practical guidelines and recommendations for traffic managers of motorways and primary roads.

Abstract

large number of pipelines in the petroleum industry simultaneously transport gas and liquid. Transient behaviour of multiphase flow is frequently encountered in these pipelines. A common example is severe slugging that can occur in multiphase flow systems where a pipeline segment with a downward or even horizontal inclination is followed by a riser segment with an upward inclination. Transient flow conditions associated with severe slugging are relatively slow. Therefore, a transient drift flux model consisting of one momentum balance equation might suffice to express the dynamics of severe slugging. We present a transient drift flux model to simulate severe slugging phenomenon in pipeline-riser systems. The present model contains recently published correlations by Shi et al. for the drift flux slip model which cover the complete range of flow conditions without introducing any discontinuities in the calculated flow parameters. Thus, the transient simulations are converging rapidly and efficiently without applying any form of smoothing. The present model is tested against experimental data for severe slugging showing a better performance than previously published models.