Matching Items (1,016)
Filtering by
- Genre: Academic theses
- All Subjects: engineering
- All Subjects: Materials Science
- Status: Published
Description
This is a two-part thesis. Part 1 presents the seasonal and tilt angle dependence of soiling loss factor of photovoltaic (PV) modules over two years for Mesa, Arizona (a desert climatic condition). Part 2 presents the development of an indoor artificial soil deposition chamber replicating natural dew cycle. Several environmental factors affect the performance of PV systems including soiling. Soiling on PV modules results in a decrease of sunlight reaching the solar cell, thereby reducing the current and power output. Dust particles, air pollution particles, pollen, bird droppings and other industrial airborne particles are some natural sources that cause soiling. The dust particles vary from one location to the other in terms of particle size, color, and chemical composition. The thickness and properties of the soil layer determine the optical path of light through the soil/glass interface. Soil accumulation on the glass surface is also influenced by environmental factors such as dew, wind speeds and rainfall. Studies have shown that soil deposition is closely related to tilt angle and exposure period before a rain event. The first part of this thesis analyzes the reduction in irradiance transmitted to a solar cell through the air/soil/glass in comparison to a clean cell (air/glass interface). A time series representation is used to compare seasonal soiling loss factors for two consecutive years (2014-2016). The effect of tilt angle and rain events on these losses are extensively analyzed. Since soiling is a significant field issue, there is a growing need to address the problem, and several companies have come up with solutions such as anti-soiling coatings, automated cleaning systems etc. To test and validate the effectiveness of these anti-soiling coating technologies, various research institutes around the world are working on the design and development of artificial indoor soiling chambers to replicate the natural process in the field. The second part of this thesis work deals with the design and development of an indoor artificial soiling chamber that replicates natural soil deposition process in the field.
ContributorsVirkar, Shalaim (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Kuitche, Joseph (Committee member) / Arizona State University (Publisher)
Created2017
Description
Advancements in thermal interface materials (TIMs) allows for the creation of new and more powerful electronics as they increase the heat transfer from the component to the heat sink. Current industrial options provide decent heat transfer, but the creation of TIMs with higher thermal conductivities is needed. In addition, if these TIMs are elastic in nature, their effectiveness can greatly increase as they can deal with changing interfaces without degradation of their properties. The research performed delves into this idea, creating elastic TIMs using liquid metal (LM), in this case galinstan, along with other matrix particles embedded in Polydimethylsiloxane (PDMS) to create an easy to use, relatively inexpensive, thermally conductive, but electrically insulative, pad with increased thermal conductivity from industrial solutions.
The pads were created using varying amounts of LM and matrix materials ranging from copper microspheres to diamond powder mixed into PDMS using a high-speed mixer. The material was then cast into molds and cured to create the pads. Once the pads were created, the difficulty came in quantifying their thermal properties. A stepped bar apparatus (SBA) following ASTM D5470 was created to measure the thermal resistance of the pads but it was determined that thermal conductivity was a more usable metric of the pads’ performance. This meant that the pad’s in-situ thickness was needed during testing, prompting the installation of a linear encoder to measure the thickness. The design and analysis of the necessary modification and proposed future design is further detailed in the following paper.
The pads were created using varying amounts of LM and matrix materials ranging from copper microspheres to diamond powder mixed into PDMS using a high-speed mixer. The material was then cast into molds and cured to create the pads. Once the pads were created, the difficulty came in quantifying their thermal properties. A stepped bar apparatus (SBA) following ASTM D5470 was created to measure the thermal resistance of the pads but it was determined that thermal conductivity was a more usable metric of the pads’ performance. This meant that the pad’s in-situ thickness was needed during testing, prompting the installation of a linear encoder to measure the thickness. The design and analysis of the necessary modification and proposed future design is further detailed in the following paper.
ContributorsKemme, Nicholas (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2017
Description
Energy performance and efficiency plays of major role in the operations of K-12 schools, as it is a significant expense and a source of budgetary pressure upon schools. Energy performance is tied to the physical infrastructure of schools, as well as the operational and behavioral patterns they accommodate. Little documentation exists within the existing literature on the measured post-occupancy performance of schools once they have begun measuring and tracking their energy performance. Further, little is known about the patterns of change over time in regard to energy performance and whether there is differentiation in these patterns between school districts.
This paper examines the annual Energy Use Intensity (EUI) of 28 different K-12 schools within the Phoenix Metropolitan Region of Arizona over the span of five years and presents an analysis of changes in energy performance resulting from the measurement of energy use in K-12 schools. This paper also analyzes the patterns of change in energy use over time and provides a comparison of these patterns by school district.
An analysis of the energy performance data for the selected schools revealed a significant positive impact on the ability for schools to improve their energy performance through ongoing performance measurement. However, while schools tend to be able to make energy improvements through the implementation of energy measurement and performance tracking, deviation may exist in their ability to maintain ongoing energy performance over time. The results suggest that implementation of ongoing measurement is likely to produce positive impacts on the energy performance of schools, however further research is recommended to enhance and refine these results.
This paper examines the annual Energy Use Intensity (EUI) of 28 different K-12 schools within the Phoenix Metropolitan Region of Arizona over the span of five years and presents an analysis of changes in energy performance resulting from the measurement of energy use in K-12 schools. This paper also analyzes the patterns of change in energy use over time and provides a comparison of these patterns by school district.
An analysis of the energy performance data for the selected schools revealed a significant positive impact on the ability for schools to improve their energy performance through ongoing performance measurement. However, while schools tend to be able to make energy improvements through the implementation of energy measurement and performance tracking, deviation may exist in their ability to maintain ongoing energy performance over time. The results suggest that implementation of ongoing measurement is likely to produce positive impacts on the energy performance of schools, however further research is recommended to enhance and refine these results.
ContributorsThurston, Anna (Author) / Sullivan, Kenneth (Thesis advisor) / Okamura, Patrick (Committee member) / Slife, Curtis (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cavitation erosion is a significant cause of wear in marine components, such as impellers, propellers or rudders. While the erosion process has been widely studied on metals, the effect of cavitation on polymers is not well-understood. The stress response in metals differs greatly from that of polymers, e.g. rate and temperature effects are far more important, thus damage and wear mechanisms of polymers under cavitating flows are significantly different. In this work, heat-driven failure caused by viscous dissipation and void nucleation resulting from tensile stresses arising from stress wave reflections are investigated as two possible material failure mechanisms.
As a first step in developing a fundamental understanding of the cavitation erosion process on polymer surfaces, simulations are performed of the collapse of individual bubbles against a compliant surface e.g. metallic substrates with polyurea coatings. The surface response of collapse-driven impact loads is represented by a idealized, time-dependent, Gaussian pressure distribution on the surface. A two-dimensional distribution of load radii and durations is considered corresponding to characteristic of cavitating flows accelerated erosion experiments. Finite element simulations are performed to fit a response curve that relates the loading parameters to the energy dissipated in the coating and integrated with collapse statistics to generate an expected heat input into the coating.
The impulsive pressure, which is generated due to bubble collapse, impacts the material and generates intense shock waves. The stress waves within the material reflects by interaction with the substrate. A transient region of high tensile stress is produced by the interaction of these waves. Simulations suggests that maximum hydrostatic tension which cause failure of polyurea layer is observed in thick coating. Also, the dissipated viscous energy and corresponding temperature rise in a polyurea is calculated, and it is concluded that temperature has influence on deformation.
As a first step in developing a fundamental understanding of the cavitation erosion process on polymer surfaces, simulations are performed of the collapse of individual bubbles against a compliant surface e.g. metallic substrates with polyurea coatings. The surface response of collapse-driven impact loads is represented by a idealized, time-dependent, Gaussian pressure distribution on the surface. A two-dimensional distribution of load radii and durations is considered corresponding to characteristic of cavitating flows accelerated erosion experiments. Finite element simulations are performed to fit a response curve that relates the loading parameters to the energy dissipated in the coating and integrated with collapse statistics to generate an expected heat input into the coating.
The impulsive pressure, which is generated due to bubble collapse, impacts the material and generates intense shock waves. The stress waves within the material reflects by interaction with the substrate. A transient region of high tensile stress is produced by the interaction of these waves. Simulations suggests that maximum hydrostatic tension which cause failure of polyurea layer is observed in thick coating. Also, the dissipated viscous energy and corresponding temperature rise in a polyurea is calculated, and it is concluded that temperature has influence on deformation.
ContributorsPanwar, Ajay (Author) / Oswald, Jay (Thesis advisor) / Dooley, Kevin (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2015
Description
Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.
ContributorsZhang, Sai (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017
Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.
ContributorsYang, Xueli (Author) / Yang, Zhihua (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Li, Qi (Committee member) / Xu, Tianfang (Committee member) / Zeng, Ruijie (Committee member) / Arizona State University (Publisher)
Created2023