# Browsing by Subject "Network modeling"

Now showing 1 - 5 of 5

- Results Per Page
1 5 10 20 40 60 80 100

- Sort Options
Ascending Descending

Item Data-driven placement of centroid connectors in dynamic traffic assignment(2016-08) James, Rachel Michelle; Boyles, Stephen David, 1982-; Gemar, Mason DShow more Recent technological advances allows transportation engineering professions to collect, share, and handle unprecedented quantities of data, which has the potential to transform current transportation planning paradigms. In the immediate future, data can be used to improve the precision and capabilities of existing transportation network modeling frameworks. Parcel data is a large, readily available data source that represents the location of public lands, businesses, and residences and is frequently used by government and businesses for land use and zoning decisions. This thesis looks at the viability of using parcel data to inform static traffic assignment (STA) and dynamic traffic assignment (DTA) connector placement in a medium sized network in the Austin, TX region. Simulation-based DTA models are particularly sensitive to the topological detail of the traffic network, including the location of centroid connectors. Traditional centroid connector placement strategies may lead to excessive congestion and unrealistic traffic patterns, while manual network refinement is prohibitive in large regional models. In this thesis, parcel-level data is used to both allocate travel demand between two sub-regions in each considered traffic analysis zone and to select appropriate nodes for the centroid connector placement. Numerical experiments suggest that the proposed approach better approximates both corridor travel times and traffic counts throughout the network, with improvements of more than 40 percent in travel time estimation accuracy, and 12 percent in traffic count estimation. Additionally, the scenarios that best matched count and travel time data were the scenarios that had the highest average parcel density per entry/exit node, indicating that parcel data is an acceptable proxy for high demand points in the network. When applied in STA, the results were not quite as promising. Although this methodology was able to improve the utilization of lower capacity links, the results ultimately did not better resemble volume count data. However, this does represent a simple, transparent, and data-driven approach for centroid connector placement in static traffic assignment that performs as well as traditional methods.Show more Item Dynamics of foam mobility in porous media(2013-05) Balan, Huseyin Onur; Nguyen, Quoc P.; Balhoff, Matthew T.Show more Foam reduces gas mobility in porous media by trapping substantial amount of gas and applying a viscous resistance of flowing lamellas to gas flow. In mechanistic foam modeling, gas relative permeability is significantly modified by gas trapping, while an effective gas viscosity, which is a function of flowing lamella density, is assigned to flowing gas. A complete understanding of foam mobility in porous media requires being able to predict the effects of pressure gradient, foam texture, rock and fluid properties on gas trapping, and therefore gas relative permeability, and effective gas viscosity. In the foam literature, separating the contributions of gas trapping and effective gas viscosity on foam mobility has not been achieved because the dynamics of gas trapping and its effects on the effective gas viscosity have been neglected. In this study, dynamics of foam mobility in porous media is investigated with a special focus on gas trapping and its effects on gas relative permeability and effective gas viscosity. Three-dimensional pore-network models representative of real porous media coupled with fluid models characterizing a lamella flow through a pore throat are used to predict flow paths, threshold pressure gradient and Darcy velocity of foam. It is found that the threshold path and the pore volume open above the threshold pressure are independent of the fluid model used in this study. Furthermore, analytical correlations of flowing gas fraction as functions of pressure gradient, lamella density, rock and fluid properties are obtained. At a constant pressure gradient, flowing gas fraction increases as overall lamella density decreases. In the discontinuous-gas foam flow regime, there exists a threshold pressure gradient, which increases with overall lamella density. One of the important findings of this study is that gas relative permeability is a strong non-linear function of flowing gas fraction, opposing most of the existing theoretical models. However, the shape of the relative gas permeability curve is poorly sensitive to overall lamella density. Flowing and trapped lamella densities change with pressure gradient. Moreover, analytical correlations of effective gas viscosity as functions of capillary number, lamella density and rock properties are obtained by up-scaling a commonly used pore-scale apparent gas (lamella) viscosity model. Effective gas viscosity increases nonlinearly with flowing lamella density, which opposes to the existing linear foam viscosity models. In addition, the individual contributions of gas trapping and effective gas viscosity on foam mobility are quantified for the first time. The functional relationship between effective gas viscosity and flowing lamella density in the presence of dynamic trapped gas is verified. A mechanistic foam model is developed by using the analytical correlations of flowing gas fraction and effective gas viscosity generated from the pore-network study and a modified population balance model. The developed model is successful in simulating unsteady-state and steady state flow of foam through porous media. Moreover, the flow behaviors in high- and low-quality flow regimes are verified by the experimental studies in the literature. Finally, the simulation results are successfully history matched with two different core-flood data.Show more Item Home therapist network modeling(2011-12) Shao, Yufen; Bard, Jonathan F.; Jarrah, Ahmad I.; Lasdon, Leon; Morton, David P.; Kutanoglu, ErhanShow more Home healthcare has been a growing sector of the economy over the last three decades with roughly 23,000 companies now doing business in the U.S. producing over $56 billion in combined annual revenue. As a highly fragmented market, profitability of individual companies depends on effective management and efficient operations. This dissertation aims at reducing costs and improving productivity for home healthcare companies. The first part of the research involves the development of a new formulation for the therapist routing and scheduling problem as a mixed integer program. Given the time horizon, a set of therapists and a group of geographically dispersed patients, the objective of the model is to minimize the total cost of providing service by assigning patients to therapists while satisfying a host of constraints concerning time windows, labor regulations and contractual agreements. This problem is NP-hard and proved to be beyond the capability of commercial solvers like CPLEX. To obtain good solutions quickly, three approaches have been developed that include two heuristics and a decomposition algorithm. The first approach is a parallel GRASP that assigns patients to multiple routes in a series of rounds. During the first round, the procedure optimizes the patient distribution among the available therapists, thus trying to reach a local optimum with respect to the combined cost of the routes. Computational results show that the parallel GRASP can reduce costs by 14.54% on average for real datasets, and works efficiently on randomly generated datasets. The second approach is a sequential GRASP that constructs one route at a time. When building a route, the procedure tracks the amount of time used by the therapists each day, giving it tight control over the treatment time distribution within a route. Computational results show that the sequential GRASP provides a cost savings of 18.09% on average for the same real datasets, but gets much better solutions with significantly less CPU for the same randomly generated datasets. The third approach is a branch and price algorithm, which is designed to find exact optima within an acceptable amount of time. By decomposing the full problem by therapist, we obtain a series of constrained shortest path problems, which, by comparison are relatively easy to solve. Computational results show that, this approach is not efficient here because: 1) convergence of Dantzig-Wolfe decomposition is not fast enough; and 2) subproblem is strongly NP-hard and cannot be solved efficiently. The last part of this research studies a simpler case in which all patients have fixed appointment times. The model takes the form of a large-scale mixed-integer program, and has different computational complexity when different features are considered. With the piece-wise linear cost structure, the problem is strongly NP-hard and not solvable with CPLEX for instances of realistic size. Subsequently, a rolling horizon algorithm, two relaxed mixed-integer models and a branch-and-price algorithm were developed. Computational results show that, both the rolling horizon algorithm and two relaxed mixed-integer models can solve the problem efficiently; the branch-and-price algorithm, however, is not practical again because the convergence of Dantzig-Wolfe decomposition is slow even when stabilization techniques are applied.Show more Item Modeling single-phase flow and solute transport across scales(2014-12) Mehmani, Yashar; Balhoff, Matthew T.Show more Flow and transport phenomena in the subsurface often span a wide range of length (nanometers to kilometers) and time (nanoseconds to years) scales, and frequently arise in applications of CO₂ sequestration, pollutant transport, and near-well acid stimulation. Reliable field-scale predictions depend on our predictive capacity at each individual scale as well as our ability to accurately propagate information across scales. Pore-scale modeling (coupled with experiments) has assumed an important role in improving our fundamental understanding at the small scale, and is frequently used to inform/guide modeling efforts at larger scales. Among the various methods, there often exists a trade-off between computational efficiency/simplicity and accuracy. While high-resolution methods are very accurate, they are computationally limited to relatively small domains. Since macroscopic properties of a porous medium are statistically representative only when sample sizes are sufficiently large, simple and efficient pore-scale methods are more attractive. In this work, two Eulerian pore-network models for simulating single-phase flow and solute transport are developed. The models focus on capturing two key pore-level mechanisms: a) partial mixing within pores (large void volumes), and b) shear dispersion within throats (narrow constrictions connecting the pores), which are shown to have a substantial impact on transverse and longitudinal dispersion coefficients at the macro scale. The models are verified with high-resolution pore-scale methods and validated against micromodel experiments as well as experimental data from the literature. Studies regarding the significance of different pore-level mixing assumptions (perfect mixing vs. partial mixing) in disordered media, as well as the predictive capacity of network modeling as a whole for ordered media are conducted. A mortar domain decomposition framework is additionally developed, under which efficient and accurate simulations on even larger and highly heterogeneous pore-scale domains are feasible. The mortar methods are verified and parallel scalability is demonstrated. It is shown that they can be used as “hybrid” methods for coupling localized pore-scale inclusions to a surrounding continuum (when insufficient scale separation exists). The framework further permits multi-model simulations within the same computational domain. An application of the methods studying “emergent” behavior during calcite precipitation in the context of geologic CO₂ sequestration is provided.Show more Item Network based prediction models for coupled transportation-epidemiological systems(2011-05) Gardner, Lauren Marie; Waller, S. Travis; Sarkar, Sahotra; Walton, Michael; Damnjanovic, Ivan; Zhang, Zhanmin; Lasdon, LeonShow more The modern multimodal transportation system provides an extensive network for human mobility and commodity exchange around the globe. As a consequence these interactions are often accompanied by disease and other biological infectious agents. This dissertation highlights the versatility of network models in quantifying the combined impact transportation systems, ecological systems and social networks have on the epidemiological process. A set of predictive models intended to compliment the current mathematical and simulation based modeling tools are introduced. The main contribution is the incorporation of dynamic infection data, which is becoming increasingly available, but is not accounted for in previous epidemiological models. Three main problems are identified. The objective of the first problem is to identify the path of infection (for a specific disease scenario) through a social contact network by invoking the use of network based optimization algorithms and individual infection reports. This problem parallels a novel and related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection. The second problem is a macroscopic application of the methodology introduced in the first problem. The new objective is to identify links in a transportation network responsible for spreading infection into new regions (spanning from a single source) using regional level infection data (e.g. when the disease arrived at a new location). The new network structure is defined by nodes which represent regions (cites, states, countries) and links representing travel routes. The third research problem is applicable to vector-borne diseases; those diseases which are transmitted to humans through the bite of an infected vector (i.e. mosquito), including dengue and malaria. The role of the vector in the infection process inherently alters the spreading process (compared to human contact diseases), which must be addressed in prediction models. The proposed objective is to quantify the risk posed by air travel in the global spread of these types of diseases.Show more