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- All Subjects: engineering
- Creators: Rogers, Bradley
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Description
The construction industry in India suffers from major time and cost overruns. Data from government and industry reports suggest that projects suffer from 20 to 25 percent time and cost overruns. Waste of resources has been identified as a major source of inefficiency. Despite a substantial increase in the past few years, demand for professionals and contractors still exceeds supply by a large margin. The traditional methods adopted in the Indian construction industry may not suffice the needs of this dynamic environment, as they have produced large inefficiencies. Innovative ways of procurement and project management can satisfy the needs aspired to as well as bring added value. The problems faced by the Indian construction industry are very similar to those faced by other developing countries. The objective of this paper is to discuss and analyze the economic concerns, inefficiencies and investigate a model that both explains the Indian construction industry structure and provides a framework to improve efficiencies. The Best Value (BV) model is examined as an approach to be adopted in lieu of the traditional approach. This could result in efficient construction projects by minimizing cost overruns and delays, which until now have been a rarity.
ContributorsNihas, Syed (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Kashiwagi, Jacob (Committee member) / Arizona State University (Publisher)
Created2013
Description
The object of this study was a 26 year old residential Photovoltaic (PV) monocrystalline silicon (c-Si) power plant, called Solar One, built by developer John F. Long in Phoenix, Arizona (a hot-dry field condition). The task for Arizona State University Photovoltaic Reliability Laboratory (ASU-PRL) graduate students was to evaluate the power plant through visual inspection, electrical performance, and infrared thermography. The purpose of this evaluation was to measure and understand the extent of degradation to the system along with the identification of the failure modes in this hot-dry climatic condition. This 4000 module bipolar system was originally installed with a 200 kW DC output of PV array (17 degree fixed tilt) and an AC output of 175 kVA. The system was shown to degrade approximately at a rate of 2.3% per year with no apparent potential induced degradation (PID) effect. The power plant is made of two arrays, the north array and the south array. Due to a limited time frame to execute this large project, this work was performed by two masters students (Jonathan Belmont and Kolapo Olakonu) and the test results are presented in two masters theses. This thesis presents the results obtained on the north array and the other thesis presents the results obtained on the south array. The resulting study showed that PV module design, array configuration, vandalism, installation methods and Arizona environmental conditions have had an effect on this system's longevity and reliability. Ultimately, encapsulation browning, higher series resistance (potentially due to solder bond fatigue) and non-cell interconnect ribbon breakages outside the modules were determined to be the primary causes for the power loss.
ContributorsBelmont, Jonathan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Henderson, Mark (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
Potential induced degradation (PID) due to high system voltages is one of the major degradation mechanisms in photovoltaic (PV) modules, adversely affecting their performance due to the combined effects of the following factors: system voltage, superstrate/glass surface conductivity, encapsulant conductivity, silicon nitride anti-reflection coating property and interface property (glass/encapsulant; encapsulant/cell; encapsulant/backsheet). Previous studies carried out at ASU's Photovoltaic Reliability Laboratory (ASU-PRL) showed that only negative voltage bias (positive grounded systems) adversely affects the performance of commonly available crystalline silicon modules. In previous studies, the surface conductivity of the glass surface was obtained using either conductive carbon layer extending from the glass surface to the frame or humidity inside an environmental chamber. This thesis investigates the influence of glass surface conductivity disruption on PV modules. In this study, conductive carbon was applied only on the module's glass surface without extending to the frame and the surface conductivity was disrupted (no carbon layer) at 2cm distance from the periphery of frame inner edges. This study was carried out under dry heat at two different temperatures (60 °C and 85 °C) and three different negative bias voltages (-300V, -400V, and -600V). To replicate closeness to the field conditions, half of the selected modules were pre-stressed under damp heat for 1000 hours (DH 1000) and the remaining half under 200 hours of thermal cycling (TC 200). When the surface continuity was disrupted by maintaining a 2 cm gap from the frame to the edge of the conductive layer, as demonstrated in this study, the degradation was found to be absent or negligibly small even after 35 hours of negative bias at elevated temperatures. This preliminary study appears to indicate that the modules could become immune to PID losses if the continuity of the glass surface conductivity is disrupted at the inside boundary of the frame. The surface conductivity of the glass, due to water layer formation in a humid condition, close to the frame could be disrupted just by applying a water repelling (hydrophobic) but high transmittance surface coating (such as Teflon) or modifying the frame/glass edges with water repellent properties.
ContributorsTatapudi, Sai Ravi Vasista (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
While the piezoelectric effect has been around for some time, it has only recently caught interest as a potential sustainable energy harvesting device. Piezoelectric energy harvesting has been developed for shoes and panels, but has yet to be integrated into a marketable bicycle tire. For this thesis, the development and feasibility of a piezoelectric tire was done. This includes the development of a circuit that incorporates piezoceramic elements, energy harvesting circuitry, and an energy storage device. A single phase circuit was designed using an ac-dc diode rectifier. An electrolytic capacitor was used as the energy storage device. A financial feasibility was also done to determine targets for manufacturing cost and sales price. These models take into account market trends for high performance tires, economies of scale, and the possibility of government subsidies. This research will help understand the potential for the marketability of a piezoelectric energy harvesting tire that can create electricity for remote use. This study found that there are many obstacles that must be addressed before a piezoelectric tire can be marketed to the general public. The power output of this device is miniscule compared to an alkaline battery. In order for this device to approach the power output of an alkaline battery the weight of the device would also become an issue. Additionally this device is very costly compared to the average bicycle tire. Lastly, this device is extreme fragile and easily broken. In order for this device to become marketable the issues of power output, cost, weight, and durability must all be successfully overcome.
ContributorsMalotte, Christopher (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
Today's energy market is facing large-scale changes that will affect all market players. Near the top of that list is the rapid deployment of residential solar photovoltaic (PV) systems. Yet that growing trend will be influenced multiple competing interests between various stakeholders, namely the utility, consumers and technology provides. This study provides a series of analyses--utility-side, consumer-side, and combined analyses--to understand and evaluate the effect of increases in residential solar PV market
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
ContributorsArnold, Michael (Author) / Johnson, Nathan G (Thesis advisor) / Rogers, Bradley (Committee member) / Ruddell, Benjamin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Research was conducted to observe the effect of Number of Transparent Covers and Refractive Index on performance of a domestic Solar Water heating system. The enhancement of efficiency for solar thermal system is an emerging challenge. The knowledge gained from this research will enable to optimize the number of transparent covers and refractive index prior to develop a solar water heater with improved optical efficiency and thermal efficiency for the collector. Numerical simulation is conducted on the performance of the liquid flat plate collector for July 21st and October 21st from 8 am to 4 pm with different refractive index values 1.1, 1.4, 1.7 and different numbers of transparent covers (0-3). In order to accomplish the proposed method the formulation and solutions are executed using simple software MATLAB. The result demonstrates efficiency of flat plate collector increases with the increase of number of covers. The performance of collector decreases when refractive index is higher. The improved useful heat gain is obtained when number of cover used is 3 and refractive index is 1.1.
ContributorsSupriti, Shahina Parvin (Author) / Rogers, Bradley (Thesis advisor) / Madakannan, Arunachalanadar (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2015
Description
This report presents the effects and analysis of the effects of Pulsed-Gas Metal Arc Welding's (P-GMAW) on Lean Duplex stainless steel. Although the welding of Duplex and Super Duplex Stainless steels have been well documented in both the laboratory and construction industry, the use of Lean Duplex has not. The purpose for conducting this research is to ensure that the correct Ferrite-Austenite phase balance along with the correct welding procedures are used in the creation of reactor cores for new construction nuclear power generation stations. In this project the effects of Lincoln Electrics ER-2209 GMAW wire are studied. Suggestions and improvements to the welding process are then proposed in order to increase the weldability, strength, gas selection, and ferrite count. The weldability will be measured using X-Ray photography in order to determine if any inclusions, lack of fusion, or voids are found post welding, along with welder feedback. The ferritic point count method in accordance with ASTM A562-08, is employed so that the amount of ferrite and austenite can be calculated in the same manor that is currently being used in industry. These will then be correlated to the tensile strength and impact toughness in the heat-affected zone (HAZ) of the weld based on the ASTM A923 testing method.
ContributorsCarter, Roger (Author) / Rogers, Bradley (Thesis advisor) / Gintz, Jerry (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2012
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
Characterization and analysis of long term field aged photovoltaic modules and encapsulant materials
Description
Photovoltaic (PV) module degradation is a well-known issue, however understanding the mechanistic pathways in which modules degrade is still a major task for the PV industry. In order to study the mechanisms responsible for PV module degradation, the effects of these degradation mechanisms must be quantitatively measured to determine the severity of each degradation mode. In this thesis multiple modules from three climate zones (Arizona, California and Colorado) were investigated for a single module glass/polymer construction (Siemens M55) to determine the degree to which they had degraded, and the main factors that contributed to that degradation. To explain the loss in power, various nondestructive and destructive techniques were used to indicate possible causes of loss in performance. This is a two-part thesis. Part 1 presents non-destructive test results and analysis and Part 2 presents destructive test results and analysis.
ContributorsChicca, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Abstract The purpose of this project is to utilize the models and concepts from Information Measurement Theory (IMT) to help minimize future decision making with respect to my career path. When I began this project, my future was clouded, my initial conditions were unknown, my stress over future career-path decisions was high, and I had eight possible career paths in mind. I have narrowed my career-path options from eight to four. In addition, I have determined a one-year plan that enables me to be prepared to pursue any of the four career paths that I have found align with me. In this project, I explored my dominant initial conditions with respect to my career path. I tracked the job history of my grandparents and parents. These efforts allowed me to identify the strengths and weaknesses that I was exhibiting by the age of three. Natural law dictates that the strengths and weaknesses of my younger self will be the same strengths and weakness that I excel at and struggle with today. I then used my understanding of natural law and the event model process to map the strengths and weaknesses of my parents and grandparents and to compare and contrast these to my strengths and weaknesses, including those that were apparent by the time that I was three years old. Focusing in on what I really want from a job, four main goals were established to grade the various future career-path options. Finally, I documented my transition from uncertainty to clarity. It began with my sobriety and ended with a milestone one-year plan that will give me information that I need to commit to my career path. This transition has had significant impact. The elusive "who am I" has been addressed, not completely but addressed sufficiently so that the question no longer plagues me. I know from where I have come. I have gained significant insight from those around me who know me. All of this has been documented for my own personal use, and for my children someday. This process permitted me to eliminate outliers from my eight original career paths, reducing them to four. In addition, application of IMT models and concepts has allowed me to see one year into the future. With my new-found knowledge, I will listen and watch the doors close on three of the remaining four career paths, as there is only one path I am meant to take.
ContributorsRichardson, Trevor Woods (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Del E. Webb Construction (Contributor)
Created2014-05