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 176
Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Production from a high pressure gas well at a high production-rate encounters the risk of operating near the choking condition for a compressible flow in porous media. The unbounded gas pressure gradient near the point of choking, which is located near the wellbore, generates an effective tensile stress on the porous rock frame. This tensile stress almost always exceeds the tensile strength of the rock and it causes a tensile failure of the rock, leading to wellbore instability. In a porous rock, not all pores are choked at the same flow rate, and when just one pore is choked, the flow through the entire porous medium should be considered choked as the gas pressure gradient at the point of choking becomes singular. This thesis investigates the choking condition for compressible gas flow in a single microscopic pore. Quasi-one-dimensional analysis and axisymmetric numerical simulations of compressible gas flow in a pore scale varicose tube with a number of bumps are carried out, and the local Mach number and pressure along the tube are computed for the flow near choking condition. The effects of tube length, inlet-to-outlet pressure ratio, the number of bumps and the amplitude of the bumps on the choking condition are obtained. These critical values provide guidance for avoiding the choking condition in practice.
ContributorsYuan, Jing (Author) / Chen, Kangping (Thesis advisor) / Wang, Liping (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
For CFD validation, hypersonic flow fields are simulated and compared with experimental data specifically designed to recreate conditions found by hypersonic vehicles. Simulated flow fields on a cone-ogive with flare at Mach 7.2 are compared with experimental data from NASA Ames Research Center 3.5" hypersonic wind tunnel. A parametric study of turbulence models is presented and concludes that the k-kl-omega transition and SST transition turbulence model have the best correlation. Downstream of the flare's shockwave, good correlation is found for all boundary layer profiles, with some slight discrepancies of the static temperature near the surface. Simulated flow fields on a blunt cone with flare above Mach 10 are compared with experimental data from CUBRC LENS hypervelocity shock tunnel. Lack of vibrational non-equilibrium calculations causes discrepancies in heat flux near the leading edge. Temperature profiles, where non-equilibrium effects are dominant, are compared with the dissociation of molecules to show the effects of dissociation on static temperature. Following the validation studies is a parametric analysis of a hypersonic inlet from Mach 6 to 20. Compressor performance is investigated for numerous cowl leading edge locations up to speeds of Mach 10. The variable cowl study showed positive trends in compressor performance parameters for a range of Mach numbers that arise from maximizing the intake of compressed flow. An interesting phenomenon due to the change in shock wave formation for different Mach numbers developed inside the cowl that had a negative influence on the total pressure recovery. Investigation of the hypersonic inlet at different altitudes is performed to study the effects of Reynolds number, and consequently, turbulent viscous effects on compressor performance. Turbulent boundary layer separation was noted as the cause for a change in compressor performance parameters due to a change in Reynolds number. This effect would not be noticeable if laminar flow was assumed. Mach numbers up to 20 are investigated to study the effects of vibrational and chemical non-equilibrium on compressor performance. A direct impact on the trends on the kinetic energy efficiency and compressor efficiency was found due to dissociation.
ContributorsOliden, Daniel (Author) / Lee, Tae-Woo (Thesis advisor) / Peet, Yulia (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
A new theoretical model was developed utilizing energy conservation methods in order to determine the fully-atomized cross-sectional Sauter mean diameters of pressure-swirl atomizers. A detailed boundary-layer assessment led to the development of a new viscous dissipation model for droplets in the spray. Integral momentum methods were also used to determine the complete velocity history of the droplets and entrained gas in the spray. The model was extensively validated through comparison with experiment and it was found that the model could predict the correct droplet size with high accuracy for a wide range of operating conditions. Based on detailed analysis, it was found that the energy model has a tendency to overestimate the droplet diameters for very low injection velocities, Weber numbers, and cone angles. A full parametric study was also performed in order to unveil some underlying behavior of pressure-swirl atomizers. It was found that at high injection velocities, the kinetic energy in the spray is significantly larger than the surface tension energy, therefore, efforts into improving atomization quality by changing the liquid's surface tension may not be the most productive. From the parametric studies it was also shown how the Sauter mean diameter and entrained velocities vary with increasing ambient gas density. Overall, the present energy model has the potential to provide quick and reasonably accurate solutions for a wide range of operating conditions enabling the user to determine how different injection parameters affect the spray quality.
ContributorsMoradi, Ali (Author) / Lee, Taewoo (Thesis advisor) / Herrmann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
The atomization of a liquid jet by a high speed cross-flowing gas has many applications such as gas turbines and augmentors. The mechanisms by which the liquid jet initially breaks up, however, are not well understood. Experimental studies suggest the dependence of spray properties on operating conditions and nozzle geom- etry. Detailed numerical simulations can offer better understanding of the underlying physical mechanisms that lead to the breakup of the injected liquid jet. In this work, detailed numerical simulation results of turbulent liquid jets injected into turbulent gaseous cross flows for different density ratios is presented. A finite volume, balanced force fractional step flow solver to solve the Navier-Stokes equations is employed and coupled to a Refined Level Set Grid method to follow the phase interface. To enable the simulation of atomization of high density ratio fluids, we ensure discrete consistency between the solution of the conservative momentum equation and the level set based continuity equation by employing the Consistent Rescaled Momentum Transport (CRMT) method. The impact of different inflow jet boundary conditions on different jet properties including jet penetration is analyzed and results are compared to those obtained experimentally by Brown & McDonell(2006). In addition, instability analysis is performed to find the most dominant insta- bility mechanism that causes the liquid jet to breakup. Linear instability analysis is achieved using linear theories for Rayleigh-Taylor and Kelvin- Helmholtz instabilities and non-linear analysis is performed using our flow solver with different inflow jet boundary conditions.
ContributorsGhods, Sina (Author) / Herrmann, Marcus (Thesis advisor) / Squires, Kyle (Committee member) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Tang, Wenbo (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Gas turbines have become widely used in the generation of power for cities. They are used all over the world and must operate under a wide variety of ambient conditions. Every turbine has a temperature at which it operates at peak capacity. In order to attain this temperature in the hotter months various cooling methods are used such as refrigeration inlet cooling systems, evaporative methods, and thermal energy storage systems. One of the more widely used is the evaporative systems because it is one of the safest and easiest to utilize method. However, the behavior of water droplets within the inlet to the turbine has not been extensively studied or documented. It is important to understand how the droplets behave within the inlet so that water droplets above a critical diameter will not enter the compressor and cause damage to the compressor blades. In order to do this a FLUENT simulation was constructed in order to determine the behavior of the water droplets and if any droplets remain at the exit of the inlet, along with their size. In order to do this several engineering drawings were obtained from SRP and studies in order to obtain the correct dimensions. Then the simulation was set up using data obtained from SRP and Parker-Hannifin, the maker of the spray nozzles. Then several sets of simulations were run in order to see how the water droplets behaved under various conditions. These results were then analyzed and quantified so that they could be easily understood. The results showed that the possible damage to the compressor increased with increasing temperature at a constant relative humidity. This is due in part to the fact that in order to keep a constant relative humidity at varying temperatures the mass fraction of water vapor in the air must be changed. As temperature increases the water vapor mass fraction must increase in order to maintain a constant relative humidity. This in turn makes it slightly increases the evaporation time of the water droplets. This will then lead to more droplets exiting the inlet and at larger diameters.
ContributorsHargrave, Kevin (Author) / Lee, Taewoo (Thesis advisor) / Huang, Huei-Ping (Committee member) / Chen, Kaangping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The partitioning of available solar energy into different fluxes at the Earth's surface is important in determining different physical processes, such as turbulent transport, subsurface hydrology, land-atmospheric interactions, etc. Direct measurements of these turbulent fluxes were carried out using eddy-covariance (EC) towers. However, the distribution of EC towers is sparse due to relatively high cost and practical difficulties in logistics and deployment. As a result, data is temporally and spatially limited and is inadequate to be used for researches at large scales, such as regional and global climate modeling. Besides field measurements, an alternative way is to estimate turbulent fluxes based on the intrinsic relations between surface energy budget components, largely through thermodynamic equilibrium. These relations, referred as relative efficiency, have been included in several models to estimate the magnitude of turbulent fluxes in surface energy budgets such as latent heat and sensible heat. In this study, three theoretical models based on the lumped heat transfer model, the linear stability analysis and the maximum entropy principle respectively, were investigated. Model predictions of relative efficiencies were compared with turbulent flux data over different land covers, viz. lake, grassland and suburban surfaces. Similar results were observed over lake and suburban surface but significant deviation is found over vegetation surface. The relative efficiency of outgoing longwave radiation is found to be orders of magnitude deviated from theoretic predictions. Meanwhile, results show that energy partitioning process is influenced by the surface water availability to a great extent. The study provides insight into what property is determining energy partitioning process over different land covers and gives suggestion for future models.
ContributorsYang, Jiachuan (Author) / Wang, Zhihua (Thesis advisor) / Huang, Huei-Ping (Committee member) / Vivoni, Enrique (Committee member) / Mays, Larry (Committee member) / Arizona State University (Publisher)
Created2012