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- Genre: Academic theses
- Creators: Huang, Huei-Ping
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
A numerical study of incremental spin-up and spin-up from rest of a thermally- stratified fluid enclosed within a right circular cylinder with rigid bottom and side walls and stress-free upper surface is presented. Thermally stratified spin-up is a typical example of baroclinity, which is initiated by a sudden increase in rotation rate and the tilting of isotherms gives rise to baroclinic source of vorticity. Research by (Smirnov et al. [2010a]) showed the differences in evolution of instabilities when Dirichlet and Neumann thermal boundary conditions were applied at top and bottom walls. Study of parametric variations carried out in this dissertation confirmed the instability patterns observed by them for given aspect ratio and Rossby number values greater than 0.5. Also results reveal that flow maintained axisymmetry and stability for short aspect ratio containers independent of amount of rotational increment imparted. Investigation on vorticity components provides framework for baroclinic vorticity feedback mechanism which plays important role in delayed rise of instabilities when Dirichlet thermal Boundary Conditions are applied.
ContributorsKher, Aditya Deepak (Author) / Chen, Kangping (Thesis advisor) / Huang, Huei-Ping (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2011
Description
Image processing in canals, rivers and other bodies of water has been a very important concern. This research using Image Processing was performed to obtain a photographic evidence of the data of the site which helps in monitoring the conditions of the water body and the surroundings. Images are captured using a digital camera and the images are stored onto a datalogger, these images are retrieved using a cellular/ satellite modem. A MATLAB program was designed to obtain the level of water by just entering the file name into to the program, a curve fit model was created to determine the contrast parameters. The contrast parameters were obtained using the data obtained from the gray scale image mainly the mean and variance of the intensity values. The enhanced images are used to determine the level of water by taking pixel intensity plots along the region of interest. The level of water obtained is accurate to less than 2% of the actual level of water observed from the image. High speed imaging in micro channels have various application in industrial field, medical field etc. In medical field it is tested by using blood samples. The experimental procedure proposed determines the flow duration and the defects observed in these channel using a fluid introduced into the micro channel the fluid being water based dye and whole milk. The viscosity of the fluid shows different types of flow patterns and defects in the micro channel. The defects observed vary from a small effect to the flow pattern to an extreme defect in the channel such as obstruction of flow or deformation in the channel. The sample needs to be further analyzed by SEM to get a better insight on the defects.
ContributorsShasedhara, Abhijeet Bangalore (Author) / Lee, Taewoo (Thesis advisor) / Huang, Huei-Ping (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2011
Description
A new method of adaptive mesh generation for the computation of fluid flows is investigated. The method utilizes gradients of the flow solution to adapt the size and stretching of elements or volumes in the computational mesh as is commonly done in the conventional Hessian approach. However, in the new method, higher-order gradients are used in place of the Hessian. The method is applied to the finite element solution of the incompressible Navier-Stokes equations on model problems. Results indicate that a significant efficiency benefit is realized.
ContributorsShortridge, Randall (Author) / Chen, Kang Ping (Thesis advisor) / Herrmann, Marcus (Thesis advisor) / Wells, Valana (Committee member) / Huang, Huei-Ping (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes an approach to system identification based on compressive sensing and demonstrates its efficacy on a challenging classical benchmark single-input, multiple output (SIMO) mechanical system consisting of an inverted pendulum on a cart. Due to its inherent non-linearity and unstable behavior, very few techniques currently exist that are capable of identifying this system. The challenge in identification also lies in the coupled behavior of the system and in the difficulty of obtaining the full-range dynamics. The differential equations describing the system dynamics are determined from measurements of the system's input-output behavior. These equations are assumed to consist of the superposition, with unknown weights, of a small number of terms drawn from a large library of nonlinear terms. Under this assumption, compressed sensing allows the constituent library elements and their corresponding weights to be identified by decomposing a time-series signal of the system's outputs into a sparse superposition of corresponding time-series signals produced by the library components. The most popular techniques for non-linear system identification entail the use of ANN's (Artificial Neural Networks), which require a large number of measurements of the input and output data at high sampling frequencies. The method developed in this project requires very few samples and the accuracy of reconstruction is extremely high. Furthermore, this method yields the Ordinary Differential Equation (ODE) of the system explicitly. This is in contrast to some ANN approaches that produce only a trained network which might lose fidelity with change of initial conditions or if facing an input that wasn't used during its training. This technique is expected to be of value in system identification of complex dynamic systems encountered in diverse fields such as Biology, Computation, Statistics, Mechanics and Electrical Engineering.
ContributorsNaik, Manjish Arvind (Author) / Cochran, Douglas (Thesis advisor) / Kovvali, Narayan (Committee member) / Kawski, Matthias (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2011
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
The evolution of single hairpin vortices and multiple interacting hairpin vortices are studied in direct numerical simulations of channel flow at Re-tau=395. The purpose of this study is to observe the effects of increased Reynolds number and varying initial conditions on the growth of hairpins and the conditions under which single hairpins autogenerate hairpin packets. The hairpin vortices are believed to provide a unified picture of wall turbulence and play an important role in the production of Reynolds shear stress which is directly related to turbulent drag. The structures of the initial three-dimensional vortices are extracted from the two-point spatial correlation of the fully turbulent direct numerical simulation of the velocity field by linear stochastic estimation and embedded in a mean flow having the profile of the fully turbulent flow. The Reynolds number of the present simulation is more than twice that of the Re-tau=180 flow from earlier literature and the conditional events used to define the stochastically estimated single vortex initial conditions include a number of new types of events such as quasi-streamwise vorticity and Q4 events. The effects of parameters like strength, asymmetry and position are evaluated and compared with existing results in the literature. This study then attempts to answer questions concerning how vortex mergers produce larger scale structures, a process that may contribute to the growth of length scale with increasing distance from the wall in turbulent wall flows. Multiple vortex interactions are studied in detail.
ContributorsParthasarathy, Praveen Kumar (Author) / Adrian, Ronald (Thesis advisor) / Huang, Huei-Ping (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2011
Description
Ten regional climate models (RCMs) and atmosphere-ocean generalized model parings from the North America Regional Climate Change Assessment Program were used to estimate the shift of extreme precipitation due to climate change using present-day and future-day climate scenarios. RCMs emulate winter storms and one-day duration events at the sub-regional level. Annual maximum series were derived for each model pairing, each modeling period; and for annual and winter seasons. The reliability ensemble average (REA) method was used to qualify each RCM annual maximum series to reproduce historical records and approximate average predictions, because there are no future records. These series determined (a) shifts in extreme precipitation frequencies and magnitudes, and (b) shifts in parameters during modeling periods. The REA method demonstrated that the winter season had lower REA factors than the annual season. For the winter season the RCM pairing of the Hadley regional Model 3 and the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model had the lowest REA factors. However, in replicating present-day climate, the pairing of the Abdus Salam International Center for Theoretical Physics' Regional Climate Model Version 3 with the Geophysical Fluid-Dynamics Laboratory atmospheric-land generalized model was superior. Shifts of extreme precipitation in the 24-hour event were measured using precipitation magnitude for each frequency in the annual maximum series, and the difference frequency curve in the generalized extreme-value-function parameters. The average trend of all RCM pairings implied no significant shift in the winter annual maximum series, however the REA-selected models showed an increase in annual-season precipitation extremes: 0.37 inches for the 100-year return period and for the winter season suggested approximately 0.57 inches for the same return period. Shifts of extreme precipitation were estimated using predictions 70 years into the future based on RCMs. Although these models do not provide climate information for the intervening 70 year period, the models provide an assertion on the behavior of future climate. The shift in extreme precipitation may be significant in the frequency distribution function, and will vary depending on each model-pairing condition. The proposed methodology addresses the many uncertainties associated with the current methodologies dealing with extreme precipitation.
ContributorsRiaño, Alejandro (Author) / Mays, Larry W. (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
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