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- All Subjects: engineering
- Creators: Sugar, Thomas
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
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The stability of nanocrystalline microstructural features allows structural materials to be synthesized and tested in ways that have heretofore been pursued only on a limited basis, especially under dynamic loading combined with temperature effects. Thus, a recently developed, stable nanocrystalline alloy is analyzed here for quasi-static (<100 s-1) and dynamic loading (103 to 104 s-1) under uniaxial compression and tension at multiple temperatures ranging from 298-1073 K. After mechanical tests, microstructures are analyzed and possible deformation mechanisms are proposed. Following this, strain and strain rate history effects on mechanical behavior are analyzed using a combination of quasi-static and dynamic strain rate Bauschinger testing. The stable nanocrystalline material is found to exhibit limited flow stress increase with increasing strain rate as compared to that of both pure, coarse grained and nanocrystalline Cu. Further, the material microstructural features, which includes Ta nano-dispersions, is seen to pin dislocation at quasi-static strain rates, but the deformation becomes dominated by twin nucleation at high strain rates. These twins are pinned from further growth past nucleation by the Ta nano-dispersions. Testing of thermal and load history effects on the mechanical behavior reveals that when thermal energy is increased beyond 200 °C, an upturn in flow stress is present at strain rates below 104 s-1. However, in this study, this simple assumption, established 50-years ago, is shown to break-down when the average grain size and microstructural length-scale is decreased and stabilized below 100nm. This divergent strain-rate behavior is attributed to a unique microstructure that alters slip-processes and their interactions with phonons; thus enabling materials response with a constant flow-stress even at extreme conditions. Hence, the present study provides a pathway for designing and synthesizing a new-level of tough and high-energy absorbing materials.
ContributorsTurnage, Scott Andrew (Author) / Solanki, Kiran N (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Peralta, Pedro (Committee member) / Darling, Kristopher A (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2017
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Increasing density of microelectronic packages, results in an increase in thermal and mechanical stresses within the various layers of the package. To accommodate the high-performance demands, the materials used in the electronic package would also require improvement. Specifically, the damage that often occurs in solders that function as die-attachment and thermal interfaces need to be addressed. This work evaluates and characterizes thermo-mechanical damage in two material systems – Electroplated Tin and Sintered Nano-Silver solder.
Tin plated electrical contacts are prone to formation of single crystalline tin whiskers which can cause short circuiting. A mechanistic model of their formation, evolution and microstructural influence is still not fully understood. In this work, growth of mechanically induced tin whiskers/hillocks is studied using in situ Nano-indentation and Electron Backscatter Diffraction (EBSD). Electroplated tin was indented and monitored in vacuum to study growth of hillocks without the influence of atmosphere. Thermal aging was done to study the effect of intermetallic compounds. Grain orientation of the hillocks and the plastically deformed region surrounding the indent was studied using Focused Ion Beam (FIB) lift-out technique. In addition, micropillars were milled on the surface of electroplated Sn using FIB to evaluate the yield strength and its relation to Sn grain size.
High operating temperature power electronics use wide band-gap semiconductor devices (Silicon Carbide/Gallium Nitride). The operating temperature of these devices can exceed 250oC, preventing use of traditional Sn-solders as Thermal Interface materials (TIM). At high temperature, the thermomechanical stresses can severely degrade the reliability and life of the device. In this light, new non-destructive approach is needed to understand the damage mechanism when subjected to reliability tests such as thermal cycling. In this work, sintered nano-Silver was identified as a promising high temperature TIM. Sintered nano-Silver samples were fabricated and their shear strength was evaluated. Thermal cycling tests were conducted and damage evolution was characterized using a lab scale 3D X-ray system to periodically assess changes in the microstructure such as cracks, voids, and porosity in the TIM layer. The evolution of microstructure and the effect of cycling temperature during thermal cycling are discussed.
Tin plated electrical contacts are prone to formation of single crystalline tin whiskers which can cause short circuiting. A mechanistic model of their formation, evolution and microstructural influence is still not fully understood. In this work, growth of mechanically induced tin whiskers/hillocks is studied using in situ Nano-indentation and Electron Backscatter Diffraction (EBSD). Electroplated tin was indented and monitored in vacuum to study growth of hillocks without the influence of atmosphere. Thermal aging was done to study the effect of intermetallic compounds. Grain orientation of the hillocks and the plastically deformed region surrounding the indent was studied using Focused Ion Beam (FIB) lift-out technique. In addition, micropillars were milled on the surface of electroplated Sn using FIB to evaluate the yield strength and its relation to Sn grain size.
High operating temperature power electronics use wide band-gap semiconductor devices (Silicon Carbide/Gallium Nitride). The operating temperature of these devices can exceed 250oC, preventing use of traditional Sn-solders as Thermal Interface materials (TIM). At high temperature, the thermomechanical stresses can severely degrade the reliability and life of the device. In this light, new non-destructive approach is needed to understand the damage mechanism when subjected to reliability tests such as thermal cycling. In this work, sintered nano-Silver was identified as a promising high temperature TIM. Sintered nano-Silver samples were fabricated and their shear strength was evaluated. Thermal cycling tests were conducted and damage evolution was characterized using a lab scale 3D X-ray system to periodically assess changes in the microstructure such as cracks, voids, and porosity in the TIM layer. The evolution of microstructure and the effect of cycling temperature during thermal cycling are discussed.
ContributorsLujan Regalado, Irene (Author) / Chawla, Nikhilesh (Thesis advisor) / Frear, Darrel (Committee member) / Rajagopalan, Jagannathan (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2018
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study uses Computational Fluid Dynamics (CFD) modeling to analyze the
dependence of wind power potential and turbulence intensity on aerodynamic design of a
special type of building with a nuzzle-like gap at its rooftop. Numerical simulations using
ANSYS Fluent are carried out to quantify the above-mentioned dependency due to three
major geometric parameters of the building: (i) the height of the building, (ii) the depth of
the roof-top gap, and (iii) the width of the roof-top gap. The height of the building is varied
from 8 m to 24 m. Likewise, the gap depth is varied from 3 m to 5 m and the gap width
from 2 m to 4 m. The aim of this entire research is to relate these geometric parameters of
the building to the maximum value and the spatial pattern of wind power potential across
the roof-top gap. These outcomes help guide the design of the roof-top geometry for wind
power applications and determine the ideal position for mounting a micro wind turbine.
From these outcomes, it is suggested that the wind power potential is greatly affected by
the increasing gap width or gap depth. It, however, remains insensitive to the increasing
building height, unlike turbulence intensity which increases with increasing building
height. After performing a set of simulations with varying building geometry to quantify
the wind power potential before the installation of a turbine, another set of simulations is
conducted by installing a static turbine within the roof-top gap. The results from the latter
are used to further adjust the estimate of wind power potential. Recommendations are made
for future applications based on the findings from the numerical simulations.
dependence of wind power potential and turbulence intensity on aerodynamic design of a
special type of building with a nuzzle-like gap at its rooftop. Numerical simulations using
ANSYS Fluent are carried out to quantify the above-mentioned dependency due to three
major geometric parameters of the building: (i) the height of the building, (ii) the depth of
the roof-top gap, and (iii) the width of the roof-top gap. The height of the building is varied
from 8 m to 24 m. Likewise, the gap depth is varied from 3 m to 5 m and the gap width
from 2 m to 4 m. The aim of this entire research is to relate these geometric parameters of
the building to the maximum value and the spatial pattern of wind power potential across
the roof-top gap. These outcomes help guide the design of the roof-top geometry for wind
power applications and determine the ideal position for mounting a micro wind turbine.
From these outcomes, it is suggested that the wind power potential is greatly affected by
the increasing gap width or gap depth. It, however, remains insensitive to the increasing
building height, unlike turbulence intensity which increases with increasing building
height. After performing a set of simulations with varying building geometry to quantify
the wind power potential before the installation of a turbine, another set of simulations is
conducted by installing a static turbine within the roof-top gap. The results from the latter
are used to further adjust the estimate of wind power potential. Recommendations are made
for future applications based on the findings from the numerical simulations.
ContributorsKailkhura, Gargi (Author) / Huang, Huei-Ping (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Very High Temperature Reactor (VHTR) is one of six conceptual designs proposed for Generation IV nuclear reactors. Alloy 617, a solid solution strengthened Ni-base superalloy, is currently the primary candidate material for the tubing of the Intermediate Heat Exchanger (IHX) in the VHTR design. Steady-state operation of the nuclear power plant at elevated temperatures leads to creep deformation, whereas loading transients including startup and shutdown generate fatigue. A detailed understanding of the creep-fatigue interaction in Alloy 617 is necessary before it can be considered as a material for nuclear construction in ASME Boiler and Pressure Vessel Code. Current design codes for components undergoing creep-fatigue interaction at elevated temperatures require creep-fatigue testing data covering the entire range from fatigue-dominant to creep-dominant loading. Classical strain-controlled tests, which produce stress relaxation during the hold period, show a saturation in cycle life with increasing hold periods due to the rapid stress-relaxation of Alloy 617 at high temperatures. Therefore, applying longer hold time in these tests cannot generate creep-dominated failure. In this study, uniaxial isothermal creep-fatigue tests with non-traditional loading waveforms were designed and performed at 850 and 950°C, with an objective of generating test data in the creep-dominant regime. The new loading waveforms are hybrid strain-controlled and force-controlled testing which avoid stress relaxation during the creep hold. The experimental data showed varying proportions of creep and fatigue damage, and provided evidence for the inadequacy of the widely-used time fraction rule for estimating creep damage under creep-fatigue conditions. Micro-scale damage features in failed test specimens, such as fatigue cracks and creep voids, were quantified using a Scanning Electron Microscope (SEM) to find a correlation between creep and fatigue damage. Quantitative statistical imaging analysis showed that the microstructural damage features (cracks and voids) are correlated with a new mechanical driving force parameter. The results from this image-based damage analysis were used to develop a phenomenological life-prediction methodology called the effective time fraction approach. Finally, the constitutive creep-fatigue response of the material at 950°C was modeled using a unified viscoplastic model coupled with a damage accumulation model. The simulation results were used to validate an energy-based constitutive life-prediction model, as a mechanistic model for potential component and structure level creep-fatigue analysis.
ContributorsTahir, Fraaz (Author) / Liu, Yongming (Thesis advisor) / Jiang, Hanqing (Committee member) / Rajagopalan, Jagannathan (Committee member) / Oswald, Jay (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2017
Description
Nanocrystalline (nc) thin films exhibit a wide range of enhanced mechanical properties compared to their coarse-grained counterparts. Furthermore, the mechanical behavior and microstructure of nc films is intimately related. Thus, precise control of the size, aspect ratio and spatial distribution of grains can enable the synthesis of thin films with exceptional mechanical properties. However, conventional bottom-up techniques for synthesizing thin films are incapable of achieving the microstructural control required to explicitly tune their properties. This dissertation focuses on developing a novel technique to synthesize metallic alloy thin films with precisely controlled microstructures and subsequently characterizing their mechanical properties using in situ transmission electron microscopy (TEM). Control over the grain size and distribution was achieved by controlling the recrystallization process of amorphous films by the use of thin crystalline seed layers. The novel technique was used to manipulate the microstructure of structural (TiAl) and functional (NiTi) thin films thereby exhibiting its capability and versatility. Following the synthesis of thin films with tailored microstructures, in situ TEM techniques were employed to probe their mechanical properties. Firstly, a novel technique was developed to measure local atomic level elastic strains in metallic glass thin films during in situ TEM straining. This technique was used to detect structural changes and anelastic deformation in metallic glass thin films. Finally, as the electron beam (e-beam) in TEMs is known to cause radiation damage to specimen, systematic experiments were carried out to quantify the effect of the e-beam on the stress-strain response of nc metals. Experiments conducted on Al and Au films revealed that the e-beam enhances dislocation activity leading to stress relaxation.
ContributorsSarkar, Rohit (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Peralta, Pedro (Committee member) / Sieradzki, Karl (Committee member) / Crozier, Peter (Committee member) / Arizona State University (Publisher)
Created2017