ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 71
Description
The implications of a changing climate have a profound impact on human life, society, and policy making. The need for accurate climate prediction becomes increasingly important as we better understand these implications. Currently, the most widely used climate prediction relies on the synthesis of climate model simulations organized by the Coupled Model Intercomparison Project (CMIP); these simulations are ensemble-averaged to construct projections for the 21st century climate. However, a significant degree of bias and variability in the model simulations for the 20th century climate is well-known at both global and regional scales. Based on that insight, this study provides an alternative approach for constructing climate projections that incorporates knowledge of model bias. This approach is demonstrated to be a viable alternative which can be easily implemented by water resource managers for potentially more accurate projections. Tests of the new approach are provided on a global scale with an emphasis on semiarid regional studies for their particular vulnerability to water resource changes, using both the former CMIP Phase 3 (CMIP3) and current Phase 5 (CMIP5) model archives. This investigation is accompanied by a detailed analysis of the dynamical processes and water budget to understand the behaviors and sources of model biases. Sensitivity studies of selected CMIP5 models are also performed with an atmospheric component model by testing the relationship between climate change forcings and model simulated response. The information derived from each study is used to determine the progressive quality of coupled climate models in simulating the global water cycle by rigorously investigating sources of model bias related to the moisture budget. As such, the conclusions of this project are highly relevant to model development and potentially may be used to further improve climate projections.
ContributorsBaker, Noel C (Author) / Huang, Huei-Ping (Thesis advisor) / Trimble, Steve (Committee member) / Anderson, James (Committee member) / Clarke, Amanda (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT A vortex tube is a device of a simple structure with no moving parts that can be used to separate a compressed gas into a hot stream and a cold stream. Many studies have been carried out to find the mechanisms of the energy separation in the vortex tube. Recent rapid development in computational fluid dynamics is providing a powerful tool to investigate the complex flow in the vortex tube. However various issues in these numerical simulations remain, such as choosing the most suitable turbulent model, as well as the lack of systematic comparative analysis. LES model for the vortex tube simulation is hardly used in the present literatures, and the influence of parameters on the performance of the vortex tube has scarcely been studied. This study is aimed to find the influence of various parameters on the performance of the vortex tube, the best geometric value of vortex tube and the realizable method to reach the required cold out flow rate 40 kg/s . First of all, setting up an original 3-D simulation vortex tube model. By comparing experiment results reported in the literature and our simulation results, a most suitable model for the simulation of the vortex tube is obtained. Secondly, we perform simulations to optimize parameters that can deliver a set of desired output, such as cold stream pressure, temperature and flow-rate. We also discuss the use of the cold air flow for petroleum engineering applications.
ContributorsCang, Ruijin (Author) / Chen, Kangping (Thesis advisor) / Huang, Hueiping (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Modern day gas turbine designers face the problem of hot mainstream gas ingestion into rotor-stator disk cavities. To counter this ingestion, seals are installed on the rotor and stator disk rims and purge air, bled off from the compressor, is injected into the cavities. It is desirable to reduce the supply of purge air as this decreases the net power output as well as efficiency of the gas turbine. Since the purge air influences the disk cavity flow field and effectively the amount of ingestion, the aim of this work was to study the cavity velocity field experimentally using Particle Image Velocimetry (PIV). Experiments were carried out in a model single-stage axial flow turbine set-up that featured blades as well as vanes, with purge air supplied at the hub of the rotor-stator disk cavity. Along with the rotor and stator rim seals, an inner labyrinth seal was provided which split the disk cavity into a rim cavity and an inner cavity. First, static gage pressure distribution was measured to ensure that nominally steady flow conditions had been achieved. The PIV experiments were then performed to map the velocity field on the radial-tangential plane within the rim cavity at four axial locations. Instantaneous velocity maps obtained by PIV were analyzed sector-by-sector to understand the rim cavity flow field. It was observed that the tangential velocity dominated the cavity flow at low purge air flow rate, its dominance decreasing with increase in the purge air flow rate. Radially inboard of the rim cavity, negative radial velocity near the stator surface and positive radial velocity near the rotor surface indicated the presence of a recirculation region in the cavity whose radial extent increased with increase in the purge air flow rate. Qualitative flow streamline patterns are plotted within the rim cavity for different experimental conditions by combining the PIV map information with ingestion measurements within the cavity as reported in Thiagarajan (2013).
ContributorsPathak, Parag (Author) / Roy, Ramendra P (Thesis advisor) / Calhoun, Ronald (Committee member) / Lee, Taewoo (Committee member) / Arizona State University (Publisher)
Created2013
Description
Climate change has been one of the major issues of global economic and social concerns in the past decade. To quantitatively predict global climate change, the Intergovernmental Panel on Climate Change (IPCC) of the United Nations have organized a multi-national effort to use global atmosphere-ocean models to project anthropogenically induced climate changes in the 21st century. The computer simulations performed with those models and archived by the Coupled Model Intercomparison Project - Phase 5 (CMIP5) form the most comprehensive quantitative basis for the prediction of global environmental changes on decadal-to-centennial time scales. While the CMIP5 archives have been widely used for policy making, the inherent biases in the models have not been systematically examined. The main objective of this study is to validate the CMIP5 simulations of the 20th century climate with observations to quantify the biases and uncertainties in state-of-the-art climate models. Specifically, this work focuses on three major features in the atmosphere: the jet streams over the North Pacific and Atlantic Oceans and the low level jet (LLJ) stream over central North America which affects the weather in the United States, and the near-surface wind field over North America which is relevant to energy applications. The errors in the model simulations of those features are systematically quantified and the uncertainties in future predictions are assessed for stakeholders to use in climate applications. Additional atmospheric model simulations are performed to determine the sources of the errors in climate models. The results reject a popular idea that the errors in the sea surface temperature due to an inaccurate ocean circulation contributes to the errors in major atmospheric jet streams.
ContributorsKulkarni, Sujay (Author) / Huang, Huei-Ping (Thesis advisor) / Calhoun, Ronald (Committee member) / Peet, Yulia (Committee member) / Arizona State University (Publisher)
Created2014
Optimization of the implementation of renewable resources in a municipal electric utility in Arizona
Description
A municipal electric utility in Mesa, Arizona with a peak load of approximately 85 megawatts (MW) was analyzed to determine how the implementation of renewable resources (both wind and solar) would affect the overall cost of energy purchased by the utility. The utility currently purchases all of its energy through long term energy supply contracts and does not own any generation assets and so optimization was achieved by minimizing the overall cost of energy while adhering to specific constraints on how much energy the utility could purchase from the short term energy market. Scenarios were analyzed for a five percent and a ten percent penetration of renewable energy in the years 2015 and 2025. Demand Side Management measures (through thermal storage in the City's district cooling system, electric vehicles, and customers' air conditioning improvements) were evaluated to determine if they would mitigate some of the cost increases that resulted from the addition of renewable resources.
In the 2015 simulation, wind energy was less expensive than solar to integrate to the supply mix. When five percent of the utility's energy requirements in 2015 are met by wind, this caused a 3.59% increase in the overall cost of energy. When that five percent is met by solar in 2015, it is estimated to cause a 3.62% increase in the overall cost of energy. A mix of wind and solar in 2015 caused a lower increase in the overall cost of energy of 3.57%. At the ten percent implementation level in 2015, solar, wind, and a mix of solar and wind caused increases of 7.28%, 7.51% and 7.27% respectively in the overall cost of energy.
In 2025, at the five percent implementation level, wind and solar caused increases in the overall cost of energy of 3.07% and 2.22% respectively. In 2025, at the ten percent implementation level, wind and solar caused increases in the overall cost of energy of 6.23% and 4.67% respectively.
Demand Side Management reduced the overall cost of energy by approximately 0.6%, mitigating some of the cost increase from adding renewable resources.
In the 2015 simulation, wind energy was less expensive than solar to integrate to the supply mix. When five percent of the utility's energy requirements in 2015 are met by wind, this caused a 3.59% increase in the overall cost of energy. When that five percent is met by solar in 2015, it is estimated to cause a 3.62% increase in the overall cost of energy. A mix of wind and solar in 2015 caused a lower increase in the overall cost of energy of 3.57%. At the ten percent implementation level in 2015, solar, wind, and a mix of solar and wind caused increases of 7.28%, 7.51% and 7.27% respectively in the overall cost of energy.
In 2025, at the five percent implementation level, wind and solar caused increases in the overall cost of energy of 3.07% and 2.22% respectively. In 2025, at the ten percent implementation level, wind and solar caused increases in the overall cost of energy of 6.23% and 4.67% respectively.
Demand Side Management reduced the overall cost of energy by approximately 0.6%, mitigating some of the cost increase from adding renewable resources.
ContributorsCadorin, Anthony (Author) / Phelan, Patrick (Thesis advisor) / Calhoun, Ronald (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2014
Description
No-confounding designs (NC) in 16 runs for 6, 7, and 8 factors are non-regular fractional factorial designs that have been suggested as attractive alternatives to the regular minimum aberration resolution IV designs because they do not completely confound any two-factor interactions with each other. These designs allow for potential estimation of main effects and a few two-factor interactions without the need for follow-up experimentation. Analysis methods for non-regular designs is an area of ongoing research, because standard variable selection techniques such as stepwise regression may not always be the best approach. The current work investigates the use of the Dantzig selector for analyzing no-confounding designs. Through a series of examples it shows that this technique is very effective for identifying the set of active factors in no-confounding designs when there are three of four active main effects and up to two active two-factor interactions.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
ContributorsKrishnamoorthy, Archana (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014
Description
Due to decrease in fossil fuel levels, the world is shifting focus towards renewable sources of energy. With an annual average growth rate of 25%, wind is one of the foremost source of harnessing cleaner energy for production of electricity. Wind turbines have been developed to tap power from wind. As a single wind turbine is insufficient, multiple turbines are installed forming a wind farm. Generally, wind farms can have hundreds to thousands of turbines concentrated in a small region. There have been multiple studies centering the influence of weather on such wind farms, but no substantial research focused on how wind farms effect local climate. Technological advances have allowed development of commercial wind turbines with a power output greater than 7.58 MW. This has led to a reduction in required number of turbines and has optimized land usage. Hence, current research considers higher power density compared to previous works that relied on wind farm density of 2 to 4 W/m 2 . Simulations were performed using Weather Research and Forecasting software provided by NCAR. The region of simulation is Southern Oregon, with domains including both onshore and offshore wind farms. Unlike most previous works, where wind farms were considered to be on a flat ground, effects of topography have also been considered here. Study of seasonal effects over wind farms has provided better insight into changes in local wind direction. Analysis of mean velocity difference across wind farms at a height of 10m and 150m gives an understanding of wind velocity profiles. Results presented in this research tends to contradict earlier belief that velocity reduces throughout the farm. Large scale simulations have shown that sometimes, more than 50% of the farm can have an increased wind velocity of up to 1m/s
at an altitude of 10m.
at an altitude of 10m.
ContributorsKadiyala, Yogesh Rao (Author) / Huang, Huei-Ping (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2015