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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
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
Engineers spend several years studying intense technical details of the processes that shape our world, yet few are exposed to classes addressing social behaviors or issues. Engineering culture creates specific barriers to addressing social science issues, such as unconscious bias, within engineering classrooms. I developed a curriculum that uses optical illusions, Legos, and the instructor's vulnerability to tackle unconscious bias in a way that addresses the barriers in engineering culture that prevent engineers from learning social science issues. Unconscious bias has documented long-term negative impacts on success and personal development, even in engineering environments. Creating a module in engineering education that addresses unconscious bias with the aim of reducing the negative effects of bias would benefit developing engineers by improving product development and team diversity. Engineering culture fosters disengagement with social issues through three pillars: depoliticization, technical/social dualism, and meritocracy. The developed curriculum uses optical illusions and Legos as proxies to start discussions about unconscious bias. The proxies allow engineers to explore their own biases without running into one of the pillars of disengagement that limits the engineer's willingness to discuss social issues. The curriculum was implemented in the Fall of 2017 in an upper-division engineering classroom as a professional communication module. The module received qualitatively positive feedback from fellow instructors and students. The curriculum was only implemented once by the author, but future implementations should be done with a different instructor and using quantitative data to measure if the learning objectives were achieved. Appendix A of the paper contains a lesson plan of the module that could be implemented by other instructors.
ContributorsLauber, Emily Anne (Author) / Wernimont, Jacqueline (Thesis director) / Mertz, Benjamin (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This dissertation treats a number of related problems in control and data analysis of complex networks.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
ContributorsWang, Lezhi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Papandreoou-Suppappola, Antonia (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017