Matching Items (52)
Filtering by
- Genre: Masters Thesis
- Creators: Bansal, Ajay
Description
Proton exchange membrane fuel cells (PEMFCs) run on pure hydrogen and oxygen (or air), producing electricity, water, and some heat. This makes PEMFC an attractive option for clean power generation. PEMFCs also operate at low temperature which makes them quick to start up and easy to handle. PEMFCs have several important limitations which must be overcome before commercial viability can be achieved. Active areas of research into making them commercially viable include reducing the cost, size and weight of fuel cells while also increasing their durability and performance. A growing and important part of this research involves the computer modeling of fuel cells. High quality computer modeling and simulation of fuel cells can help speed up the discovery of optimized fuel cell components. Computer modeling can also help improve fundamental understanding of the mechanisms and reactions that take place within the fuel cell. The work presented in this thesis describes a procedure for utilizing computer modeling to create high quality fuel cell simulations using Ansys Fluent 12.1. Methods for creating computer aided design (CAD) models of fuel cells are discussed. Detailed simulation parameters are described and emphasis is placed on establishing convergence criteria which are essential for producing consistent results. A mesh sensitivity study of the catalyst and membrane layers is presented showing the importance of adhering to strictly defined convergence criteria. A study of iteration sensitivity of the simulation at low and high current densities is performed which demonstrates the variance in the rate of convergence and the absolute difference between solution values derived at low numbers of iterations and high numbers of iterations.
ContributorsArvay, Adam (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Liang, Yong (Committee member) / Subach, James (Committee member) / Arizona State University (Publisher)
Created2011
Description
Dye sensitized solar cells (DSSCs) are the third generation solar cells expected to outperform the first two generations of solar cells with their advantages of comparative higher efficiency and lower manufacturing costs. The manufacturing cost of Dye sensitized solar cells is one fifth of the conventional silicon solar cell. However, DSSCs have problems of low conversion efficiency, stability and reliability. Some effective approaches are required to improve their performance. This paper projects the work related to assessment and verification of the repeatability of the semi-automated fabrication process. Changes were introduced in to the fabrication process to enhance the efficiency and stability. The sealant step in the fabrication process was remodeled to a newer version with an improvement in efficiency from 11% to 11.8%. Sputtering was performed on counter electrode in 30 seconds intervals. Cells were fabricated to assess the performance & time dependent characteristics from EIS experiments. Series resistance increased three times in sputtered Pt electrode as compared to standard platinum electrode. This resulted in the degradation of conductive surface on glass electrode due to heavy bombardment of ions. The second phase of the project work relates to the incorporation of SWCNT on the TiO2 electrode and its effect on the cell efficiency. Different weight loadings (0.1 wt %, 0.2 wt%, 0.4 wt %) of SWCNTs were prepared and mixed with the commercial TiO2 paste and ethanol solvent. The TiO2-SWCNT layer was coated on the electrode using screen-printing technique. Both open circuit voltage and photocurrent were found to have measurable dependence on the TiO2 layer loading. Photo voltage ranged from ~0.73 V to ~0.43 V and photocurrent from ~8 to ~33 mA depending on weight percent loading. This behavior is due to aggregation of particles and most TiO2 aggregate particles are not connected to SWCNT. Transparency loss was observed leading to saturation in the photo current and limiting the light absorption within the TiO2 film.
ContributorsKinhal, Kartik (Author) / Munukutla, Lakshmi V (Thesis advisor) / Subach, James (Committee member) / Peng, Xihong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Battery performance has been studied at different temperature, C rate. Different types of batteries have been used. Capacity and impedance are two factors, which are focused in the thesis. To evaluate battery performance and battery conditions, the SOC (state of charge) determination methods have been studied in the thesis. There are two types of batteries divided in three groups: group I. Ni-Cd battery (2V, 8Ah); group II. Lead-acid battery (2V, 8Ah); and group III. Lead-acid battery (2V, 25Ah). The impedance testing is using electrochemical impedance spectroscopy methods. AC impedance method has been used to test different state of charge (100%, 80%, 60%, 40%, 20%). For the corrosion part, the corrosion rate of metal material in the heat transfer fluids has been tested at different temperature. Hastelloys C-276 in eutectic molten salts a mixture of NaCl, KCl and ZnCl2 using potentiodynamic method (swap from ± 30 mV in 0.2 mV.s-1). The lowest corrosion rate of Hastelloy C-276 is 5.51 µm per year at 250 °C. Particularly, the corrosion rate of Hastelloy C-276 jumps up to 53.33 µm per year at 400 °C.
ContributorsChu, Ximo (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Nam, Changho (Committee member) / Arizona State University (Publisher)
Created2013
Description
Microgrids are a subset of the modern power structure; using distributed generation (DG) to supply power to communities rather than vast regions. The reduced scale mitigates loss allowing the power produced to do more with better control, giving greater security, reliability, and design flexibility. This paper explores the performance and cost viability of a hybrid grid-tied microgrid that utilizes Photovoltaic (PV), batteries, and fuel cell (FC) technology. The concept proposes that each community home is equipped with more PV than is required for normal operation. As the homes are part of a microgrid, excess or unused energy from one home is collected for use elsewhere within the microgrid footprint. The surplus power that would have been discarded becomes a community asset, and is used to run intermittent services. In this paper, the modeled community does not have parking adjacent to each home allowing for the installment of a privately owned slower Level 2 charger, making EV ownership option untenable. A solution is to provide a Level 3 DC Quick Charger (DCQC) as the intermittent service. The addition of batteries and Fuel Cells are meant to increase load leveling, reliability, and instill limited island capability.
ContributorsPatterson, Maxx (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Macia, Narciso (Committee member) / Peng, Xihong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Smart home system (SHS) is a kind of information system aiming at realizing home automation. The SHS can connect with almost any kind of electronic/electric device used in a home so that they can be controlled and monitored centrally. Today's technology also allows the home owners to control and monitor the SHS installed in their homes remotely. This is typically realized by giving the SHS network access ability. Although the SHS's network access ability brings a lot of conveniences to the home owners, it also makes the SHS facing more security threats than ever before. As a result, when designing a SHS, the security threats it might face should be given careful considerations. System security threats can be solved properly by understanding them and knowing the parts in the system that should be protected against them first. This leads to the idea of solving the security threats a SHS might face from the requirements engineering level. Following this idea, this paper proposes a systematic approach to generate the security requirements specifications for the SHS. It can be viewed as the first step toward the complete SHS security requirements engineering process.
ContributorsXu, Rongcao (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
Description
With a recent shift to a more environmentally conscious society, low-carbon and non-carbon producing energy production methods are being investigated and applied all over the world. Of these methods, fuel cells show great potential for clean energy production. A fuel cell is an electrochemical energy conversion device which directly converts chemical energy into electrical energy. Proton exchange membrane fuel cells (PEMFCs) are a highly researched energy source for automotive and stationary power applications. In order to produce the power required to meet Department of Energy requirements, platinum (Pt) must be used as a catalyst material in PEMFCs. Platinum, however, is very expensive and extensive research is being conducted to develop ways to reduce the amount of platinum used in PEMFCs. In the current study, three catalyst synthesis techniques were investigated and evaluated on their effectiveness to produce platinum-on copper (Pt@Cu) core-shell nanocatalyst on multi-walled carbon nanotube (MWCNT) support material. These three methods were direct deposition method, two-phase surfactant method, and single-phase surfactant method, in which direct deposition did not use a surfactant for particle size control and the surfactant methods did. The catalyst materials synthesized were evaluated by visual inspection and fuel cell performance. Samples which produced high fuel cell power output were evaluated using transmission electron microscopy (TEM) imaging. After evaluation, it was concluded that the direct deposition technique was effective in synthesizing Pt@Cu core-shell nanocatalyst on MWCNTs support when a rinsing process was used before adding platinum. The peak power density achieved by the rinsed core-shell catalyst was 618 mW.cm-2 , 13 percent greater than that of commercial platinum-carbon (Pt/C) catalyst. Transmission electron microscopy imaging revealed the core-shell catalyst contained Pt shells and platinum-copper alloy cores. Rinsing with deionized (DI) water was shown to be a crucial step in core-shell catalyst deposition as it reduced the number of platinum colloids on the carbon nanotube surface. After evaluation, it was concluded that the two-phase surfactant and single-phase surfactant synthesis methods were not effective at producing core-shell nanocatalyst with the parameters investigated.
ContributorsAdame, Anthony (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Tamizhmani, Govindasamy (Committee member) / Arizona State University (Publisher)
Created2012
Description
Gas diffusion layers (GDLs) are a critical and essential part of proton exchange membrane fuel cells (PEMFCs). They carry out various important functions such as transportation of reactants to and from the reaction sites. The material properties and structural characteristics of the substrate and the microporous layer strongly influence fuel cell performance. The microporous layer of the GDLs was fabricated with the carbon slurry dispersed in water containing ammonium lauryl sulfate (ALS) using the wire rod coating method. GDLs were fabricated with different materials to compose the microporous layer and evaluated the effects on PEMFC power output performance. The consistency of the carbon slurry was achieved by adding 25 wt. % of PTFE, a binding agent with a 75:25 ratio of carbon (Pureblack and vapor grown carbon fiber). The GDLs were investigated in PEMFC under various relative humidity (RH) conditions using H2/O2 and H2/Air. GDLs were also fabricated with the carbon slurry dispersed in water containing sodium dodecyl sulfate (SDS) and multiwalled carbon nanotubes (MWCNTs) with isopropyl alcohol (IPA) based for fuel cell performance comparison. MWCNTs and SDS exhibits the highest performance at 60% and 70% RH with a peak power density of 1100 mW.cm-2 and 850 mW.cm-2 using air and oxygen as an oxidant. This means that the gas diffusion characteristics of these two samples were optimum at 60 and 70 % RH with high limiting current density range. It was also found that the composition of the carbon slurry, specifically ALS concentration has the highest peak power density of 1300 and 500mW.cm-2 for both H2/O2 and H2/Air at 100% RH. However, SDS and MWCNTs demonstrates the lowest power density using air and oxygen as an oxidants at 100% RH.
ContributorsVillacorta, Rashida (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Tamizhmani, Govindasamy (Committee member) / Arizona State University (Publisher)
Created2012
Description
Gathering and managing software requirements, known as Requirement Engineering (RE), is a significant and basic step during the Software Development Life Cycle (SDLC). Any error or defect during the RE step will propagate to further steps of SDLC and resolving it will be more costly than any defect in other steps. In order to produce better quality software, the requirements have to be free of any defects. Verification and Validation (V&V;) of requirements are performed to improve their quality, by performing the V&V; process on the Software Requirement Specification (SRS) document. V&V; of the software requirements focused to a specific domain helps in improving quality. A large database of software requirements from software projects of different domains is created. Software requirements from commercial applications are focus of this project; other domains embedded, mobile, E-commerce, etc. can be the focus of future efforts. The V&V; is done to inspect the requirements and improve the quality. Inspections are done to detect defects in the requirements and three approaches for inspection of software requirements are discussed; ad-hoc techniques, checklists, and scenario-based techniques. A more systematic domain-specific technique is presented for performing V&V; of requirements.
ContributorsChughtai, Rehman (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Millard, Bruce (Committee member) / Arizona State University (Publisher)
Created2012
Description
Calculus as a math course is important subject students need to succeed in, in order to venture into STEM majors. This thesis focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed in the course. Calculus has high failure rates which corroborates with the data collected from Arizona State University that shows that 40% of the 3266 students whose data were used failed in their calculus course.This thesis proposes to utilize educational big data to detect students at high risk of failure and their eventual early detection and subsequent intervention can be useful. Some existing studies similar to this thesis make use of open-scale data that are lower in data count and perform predictions on low-impact Massive Open Online Courses(MOOC) based courses. In this thesis, an automatic detection method of academically at-risk students by using learning management systems(LMS) activity data along with the student information system(SIS) data from Arizona State University(ASU) for the course calculus for engineers I (MAT 265) is developed. The method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student.
This thesis also proposes a new technique to convert this button click data into a button click sequence which can be used as inputs to classifiers. In addition, the advancements in Natural Language Processing field can be used by adopting methods such as part-of-speech (POS) tagging and tools such as Facebook Fasttext word embeddings to convert these button click sequences into numeric vectors before feeding them into the classifiers. The thesis proposes two preprocessing techniques and evaluates them on 3 different machine learning ensembles to determine their performance across the two modalities of the class.
ContributorsDileep, Akshay Kumar (Author) / Bansal, Ajay (Thesis advisor) / Cunningham, James (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2021
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021