Comparison of computational and analytical methods for evaluation of failure pressure of subsea pipelines containing internal and external corrosions

In the designing stage of subsea pipelines, the design parameters, such as pipe materials, thickness and diameters, are carefully determined to guarantee flow assurance and structural safety. However, once corrosion occurs in pipelines, the operating pressure should be decreased to prevent the failure of pipelines. Otherwise, an abrupt burst can occur in the corroded region of the pipeline, and it leads to serious disasters in the environment and financial loss. Accordingly, the relationship between the corrosion amount and failure pressure of the pipeline, i.e., the maximum operating pressure, should be investigated, and then, the assessment guideline considering the failure pressure should be identified. There are several explicit type codes that regulate the structural safety for corroded subsea pipelines, such as ASME B31G, DNV RF 101, ABS Building and Classing Subsea Pipeline Systems, and API 579. These rules are well defined; however, there are some limitations associated with describing precise failure pressure. Briefly, all of the existing rules cannot consider the material nonlinearity, such as elastoplasticity effect of the pipeline, as well as the actual three-dimensional corrosion shape. Therefore, the primary aim of this study is to suggest a modified formula parameter considering the above-mentioned pipeline and corrosion characteristics. As a result, the material nonlinearity as well as the corrosion configuration, i.e., axial/circumferential corrosion length, width and depth, is reflected in a set of finite element models and a series of finite element analysis considering the operation conditions are followed. Based on the comparative study between the simulation and analytical results, which can be obtained from the classification society rules, the modified formulae for failure pressure calculation are proposed.     

Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips

This paper analyzes transportation mode choice for short home-based trips using a 1999 activity survey from the Puget Sound region of Washington State, U.S.A. Short trips are defined as those within the 95th percentile walking distance in the data, here 1.40 miles (2.25 km). The mean walking distance was 0.4 miles (0.6 km). The mode distribution was automobile (75%), walk (23%), bicycle (1%), and bus (1%). Walk and bicycle are found less likely as the individual’s age increases. People are more likely to drive if they can or are accustomed to. People in multi-person families are less likely to walk or use bus, especially families with children. An environment that attracts people’s interest and provides activity opportunities encourages people to walk on short trips. Influencing people’s choice of transport mode on short trips should be an important part of efforts encouraging the use of non-automobile alternatives.

Design of a compressor-power-based exhaust manifold pressure estimator for diesel engine air management

In accordance with the development of hardware configurations in diesel engines, research on model-based control for these systems has been conducted for years. To control the air management system of a diesel engine, the exhaust manifold pressure should be selected as one of the control targets due to its internal dynamic stability and its physical importance in model-based control. However, it is difficult to measure exhaust pressure using sensors due to gas flow oscillation in the exhaust manifold in a reciprocated diesel engine. Moreover, the sensor is too costly to be equipped on production engines. Hence, the estimation strategies for exhaust manifold pressure have been regarded as a primary issue in diesel engine air management control. This paper proposes a new estimation method for determining the exhaust manifold pressure based on compressor power dynamics. With its simple and robust structure, this estimation leads to improved control performance compared with that of general observers. To compensate for the compressor efficiency error that varies with turbine speed, some correction maps are adopted in the compressor power equation. To verify the control system performance with the new estimator, a HiLS (hardware in the loop simulation) of the NRTC mode is performed. Experimental verification is also conducted using a test bench for the C1-08 mode.     

Fatigue life estimation of injection mold core using simulation-based approach

The purpose of this study is to present a simulation-based approach capable of predicting the fatigue life of a mold core with the help of CAE technology based on injection molding simulation, core stress analysis and fatigue life prediction. Injection molding simulation is performed in order to obtain the non-uniform pressure acting on a mold core due to the polymer melt flow during injection molding process. Subsequently, the melt pressure profile is used as loading condition for core stress analysis via one-way coupling technique. The lifetime of a mold core is then predicted by fatigue analysis with stress-life approach. For verification of simulation-based approach to the fatigue life prediction, tests were also performed.     

Development of estimation algorithms for vehicle’s mass and road grade

In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.     

Active noise control in a duct system based on a frequency-estimation algorithm and the FX-LMS algorithm

In this paper, an indirect feedback active noise control (ANC) scheme, based on the fundamental frequency-estimation method, is proposed for systems with multiple tonal noises. The engine noise consists of the harmonic components of the rotation of the crankshaft in the engine. When it is difficult to obtain reference signals, which are necessary for a feedforward ANC, conventional ANC algorithms do not work. In this paper, a new method is proposed to generate reference signals with estimated frequencies. The proposed algorithm consists of two parts: the frequency-estimation (FE) algorithm for the estimation of the fundamental frequency of the rotating machinery and then the conventional filtered-x LMS (FX-LMS) algorithm. In the second algorithm, the reference signal is generated using the fundamental frequency as estimated in the first algorithm. The FE algorithm uses a second-order adaptive notch filter, which is insensitive to impulse noise. In addition, the FE algorithm has good tracking capability and a lower variance of frequency-estimation error for a constant sinusoid signal and chirp signal. The performance of the proposed ANC method is verified through simulations and an experiment using a DSP board (DS-1104) inside a short duct.     

Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models

Abstract

Online traffic flow modeling is of increasing importance due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper, the dynamic flow-density relation is identified based on the classification of traffic states and is quantified employing fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.     

A comparative study of aggregate and disaggregate gravity models using Seoul metropolitan subway trip data

Abstract

Existing origin constrained and doubly constrained gravity models have not been compared, theoretically or empirically, in terms of their forecasting power. Due to the newly advanced technology of intelligent transport systems, the expanded data presently available have made various models more comparable in terms of forecasting power. This paper uses archived automatic passenger counting (APC) data for urban rail in the Seoul metropolitan area. The APC data contains information about each trip's origin, destination, ticket type, fare, and distance on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate gravity modeling using these data. A Hyman aggregate gravity model is used as the aggregate model without the spatial effect. The disaggregate model adopts a multinomial logit as the destination choice model with the spatial effect. In general, while the formulation of aggregate and disaggregate gravity model models are similar, the calibration and parameter estimation methods of the two models are different. As a result, this empirical study demonstrates that the variation in goodness-of-fit and forecasting power largely depends on the estimation method and selected variables. The forecasting power of the disaggregate modeling approach outperforms that of the aggregate model. This paper further confirms that spatial arrangement plays important roles in gravity modeling.     

A structural equation model of activity participation and travel behavior using longitudinal data

Abstract

This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that people's current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4.’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.