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Modeling Process (modeling + process)
Selected AbstractsAnalysis of a Vertical Dipole Tracer Test in Highly Fractured RockGROUND WATER, Issue 5 2002William E. Sanford The results of a vertical dipole tracer experiment performed in highly fractured rocks of the Clare Valley, South Australia, are presented. The injection and withdrawal piezometers were both screened over 3 m and were separated by 6 m (midpoint to midpoint). Due to the long screen length, several fracture sets were intersected, some of which do not connect the two piezometers. Dissolved helium and bromide were injected into the dipole flow field for 75 minutes, followed by an additional 510 minutes of flushing. The breakthrough of helium was retarded relative to bromide, as was expected due to the greater aqueous diffusion coefficient of helium. Also, only 25% of the total mass injected of both tracers was recovered. Modeling of the tracer transport was accomplished using an analytical one-dimensional flow and transport model for flow through a fracture with diffusion into the matrix. The assumptions made include: streamlines connecting the injection and withdrawal point can be modeled as a dipole of equal strength, flow along each streamline is one dimensional, and there is a constant Peclet number for each streamline. In contrast to many other field tracer studies performed in fractured rock, the actual travel length between piezometers was not known. Modeling was accomplished by fitting the characteristics of the tracer breakthrough curves (BTCs), such as arrival times of the peak concentration and the center of mass. The important steps were to determine the fracture aperture (240 ,m) based on the parameters that influence the rate of matrix diffusion (this controls the arrival time of the peak concentration); estimating the travel distance (11 m) by fitting the time of arrival of the centers of mass of the tracers; and estimating fracture dispersivity (0.5 m) by fitting the times that the inflection points occurred on the front and back limbs of the BTCs. This method works even though there was dilution in the withdrawal well, the amount of which can be estimated by determining the value that the modeled concentrations need to be reduced to fit the data (,50%). The use of two tracers with different diffusion coefficients was not necessary, but it provides important checks in the modeling process because the apparent retardation between the two tracers is evidence of matrix diffusion and the BTCs of both tracers need to be accurately modeled by the best fit parameters. [source] Development of an optimization model for energy systems planning in the Region of WaterlooINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 11 2008Y. P. Cai Abstract In this study, a large-scale dynamic optimization model (University of Regina Energy Model, UREM) has been developed for supporting long-term energy systems planning in the Region of Waterloo. The model can describe energy management systems as networks of a series of energy flows, transferring extracted/imported energy resources to end users through a variety of conversion and transmission technologies over a number of periods. It can successfully incorporate optimization models, scenario development and policy analysis within a general framework. Complexities in energy management systems can be systematically reflected; thus, the applicability of the modeling process can be highly enhanced. Four scenarios (including a reference case) are considered based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting trade-offs among energy-related, environmental and economic considerations. They are helpful for supporting (a) adjustment or justification of the existing allocation patterns of energy resources and services, (b) allocations of renewable energy resources, (c) formulation of local policies regarding energy consumption, economic development and energy structure, and (d) analysis of interactions among economic cost, system efficiency, emission mitigation and energy-supply security. Results also indicate that UREM can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies and emission reduction measures within an integrated and dynamic framework. Copyright © 2008 John Wiley & Sons, Ltd. [source] Estimating time dependent O-D trip tables during peak periodsJOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2000Srinivas S. Pulugurtha Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [source] Competing Risks and Time-Dependent CovariatesBIOMETRICAL JOURNAL, Issue 1 2010Giuliana Cortese Abstract Time-dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time-fixed covariates. This study briefly recalls the different types of time-dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time-dependent covariates are to be included in the modeling process, then it is still possible to estimate cause-specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi-state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34, 70,85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause-specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods. [source] Purpose-Based Expert Finding in a Portfolio Management SystemCOMPUTATIONAL INTELLIGENCE, Issue 4 2004Xiaolin Niu Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent-based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose-based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context-dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies. [source] | ||||||||||