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- All Subjects: Mechanical Engineering
- Genre: Doctoral Dissertation
- Creators: Phelan, Patrick
- Creators: Berman, Spring
Description
Locomotion of microorganisms is commonly observed in nature. Although microorganism locomotion is commonly attributed to mechanical deformation of solid appendages, in 1956 Nobel Laureate Peter Mitchell proposed that an asymmetric ion flux on a bacterium's surface could generate electric fields that drive locomotion via self-electrophoresis. Recent advances in nanofabrication have enabled the engineering of synthetic analogues, bimetallic colloidal particles, that swim due to asymmetric ion flux originally proposed by Mitchell. Bimetallic colloidal particles swim through aqueous solutions by converting chemical fuel to fluid motion through asymmetric electrochemical reactions. This dissertation presents novel bimetallic motor fabrication strategies, motor functionality, and a study of the motor collective behavior in chemical concentration gradients. Brownian dynamics simulations and experiments show that the motors exhibit chemokinesis, a motile response to chemical gradients that results in net migration and concentration of particles. Chemokinesis is typically observed in living organisms and distinct from chemotaxis in that there is no particle directional sensing. The synthetic motor chemokinesis observed in this work is due to variation in the motor's velocity and effective diffusivity as a function of the fuel and salt concentration. Static concentration fields are generated in microfluidic devices fabricated with porous walls. The development of nanoscale particles that swim autonomously and collectively in chemical concentration gradients can be leveraged for a wide range of applications such as directed drug delivery, self-healing materials, and environmental remediation.
ContributorsWheat, Philip Matthew (Author) / Posner, Jonathan D (Thesis advisor) / Phelan, Patrick (Committee member) / Chen, Kangping (Committee member) / Buttry, Daniel (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
As one of the most promising materials for high capacity electrode in next generation of lithium ion batteries, silicon has attracted a great deal of attention in recent years. Advanced characterization techniques and atomic simulations helped to depict that the lithiation/delithiation of silicon electrode involves processes including large volume change (anisotropic for the initial lithiation of crystal silicon), plastic flow or softening of material dependent on composition, electrochemically driven phase transformation between solid states, anisotropic or isotropic migration of atomic sharp interface, and mass diffusion of lithium atoms. Motivated by the promising prospect of the application and underlying interesting physics, mechanics coupled with multi-physics of silicon electrodes in lithium ion batteries is studied in this dissertation. For silicon electrodes with large size, diffusion controlled kinetics is assumed, and the coupled large deformation and mass transportation is studied. For crystal silicon with small size, interface controlled kinetics is assumed, and anisotropic interface reaction is studied, with a geometry design principle proposed. As a preliminary experimental validation, enhanced lithiation and fracture behavior of silicon pillars via atomic layer coatings and geometry design is studied, with results supporting the geometry design principle we proposed based on our simulations. Through the work documented here, a consistent description and understanding of the behavior of silicon electrode is given at continuum level and some insights for the future development of the silicon electrode are provided.
ContributorsAn, Yonghao (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Phelan, Patrick (Committee member) / Wang, Yinming (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2014
Description
Portable devices rely on battery systems that contribute largely to the overall device form factor and delay portability due to recharging. Membraneless microfluidic fuel cells are considered as the next generation of portable power sources for their compatibility with higher energy density reactants. Microfluidic fuel cells are potentially cost effective and robust because they use low Reynolds number flow to maintain fuel and oxidant separation instead of ion exchange membranes. However, membraneless fuel cells suffer from poor efficiency due to poor mass transport and Ohmic losses. Current microfluidic fuel cell designs suffer from reactant cross-diffusion and thick boundary layers at the electrode surfaces, which result in a compromise between the cell's power output and fuel utilization. This dissertation presents novel flow field architectures aimed at alleviating the mass transport limitations. The first architecture provides a reactant interface where the reactant diffusive concentration gradients are aligned with the bulk flow, mitigating reactant mixing through diffusion and thus crossover. This cell also uses porous electro-catalysts to improve electrode mass transport which results in higher extraction of reactant energy. The second architecture uses porous electrodes and an inert conductive electrolyte stream between the reactants to enhance the interfacial electrical conductivity and maintain complete reactant separation. This design is stacked hydrodynamically and electrically, analogous to membrane based systems, providing increased reactant utilization and power. These fuel cell architectures decouple the fuel cell's power output from its fuel utilization. The fuel cells are tested over a wide range of conditions including variation of the loads, reactant concentrations, background electrolytes, flow rates, and fuel cell geometries. These experiments show that increasing the fuel cell power output is accomplished by increasing reactant flow rates, electrolyte conductivity, and ionic exchange areas, and by decreasing the spacing between the electrodes. The experimental and theoretical observations presented in this dissertation will aid in the future design and commercialization of a new portable power source, which has the desired attributes of high power output per weight and volume and instant rechargeability.
ContributorsSalloum, Kamil S (Author) / Posner, Jonathan D (Thesis advisor) / Adrian, Ronald (Committee member) / Christen, Jennifer (Committee member) / Phelan, Patrick (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2010
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
In convective heat transfer processes, heat transfer rate increases generally with a large fluid velocity, which leads to complex flow patterns. However, numerically analyzing the complex transport process and conjugated heat transfer requires extensive time and computing resources. Recently, data-driven approach has risen as an alternative method to solve physical problems in a computational efficient manner without necessitating the iterative computations of the governing physical equations. However, the research on data-driven approach for convective heat transfer is still in nascent stage. This study aims to introduce data-driven approaches for modeling heat and mass convection phenomena. As the first step, this research explores a deep learning approach for modeling the internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. A trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu) and friction factor (f) of a flow in a heated channel over Reynolds number (Re) ranging from 100 to 27750. The optimized cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. Next, this research introduces a deep learning based surrogate model for three-dimensional (3D) transient mixed convention in a horizontal channel with a heated bottom surface. Conditional generative adversarial networks (cGAN) are trained to approximate the temperature maps at arbitrary channel locations and time steps. The model is developed for a mixed convection occurring at the Re of 100, Rayleigh number of 3.9E6, and Richardson number of 88.8. The cGAN with the PatchGAN based classifier without the strided convolutions infers the temperature map with the best clarity and accuracy.
Finally, this study investigates how machine learning analyzes the mass transfer in 3D printed fluidic devices. Random forests algorithm is hired to classify the flow images taken from semi-transparent 3D printed tubes. Particularly, this work focuses on laminar-turbulent transition process occurring in a 3D wavy tube and a straight tube visualized by dye injection. The machine learning model automatically classifies experimentally obtained flow images with an accuracy > 0.95.
ContributorsKang, Munku (Author) / Kwon, Beomjin (Thesis advisor) / Phelan, Patrick (Committee member) / Ren, Yi (Committee member) / Rykaczewski, Konrad (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autonomous systems inevitably must interact with other surrounding systems; thus, algorithms for intention/behavior estimation are of great interest. This thesis dissertation focuses on developing passive and active model discrimination algorithms (PMD and AMD) with applications to set-valued intention identification and fault detection for uncertain/bounded-error dynamical systems. PMD uses the obtained input-output data to invalidate the models, while AMD designs an auxiliary input to assist the discrimination process. First, PMD algorithms are proposed for noisy switched nonlinear systems constrained by metric/signal temporal logic specifications, including systems with lossy data modeled by (m,k)-firm constraints. Specifically, optimization-based algorithms are introduced for analyzing the detectability/distinguishability of models and for ruling out models that are inconsistent with observations at run time. On the other hand, two AMD approaches are designed for noisy switched nonlinear models and piecewise affine inclusion models, which involve bilevel optimization with integer variables/constraints in the inner/lower level. The first approach solves the inner problem using mixed-integer parametric optimization, whose solution is included when solving the outer problem/higher level, while the second approach moves the integer variables/constraints to the outer problem in a manner that retains feasibility and recasts the problem as a tractable mixed-integer linear programming (MILP). Furthermore, AMD algorithms are proposed for noisy discrete-time affine time-invariant systems constrained by disjunctive and coupled safety constraints. To overcome the issues associated with generalized semi-infinite constraints due to state-dependent input constraints and disjunctive safety constraints, several constraint reformulations are proposed to recast the AMD problems as tractable MILPs. Finally, partition-based AMD approaches are proposed for noisy discrete-time affine time-invariant models with model-independent parameters and output measurement that are revealed at run time. Specifically, algorithms with fixed and adaptive partitions are proposed, where the latter improves on the performance of the former by allowing the partitions to be optimized. By partitioning the operation region, the problem is solved offline, and partition trees are constructed which can be used as a `look-up table' to determine the optimal input depending on revealed information at run time.
ContributorsNiu, Ruochen (Author) / Yong, Sze Zheng S.Z. (Thesis advisor) / Berman, Spring (Committee member) / Ren, Yi (Committee member) / Zhang, Wenlong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2022
Description
Tire blowout often occurs during driving, which can suddenly disturb vehicle motions and seriously threaten road safety. Currently, there is still a lack of effective methods to mitigate tire blowout risks in everyday traffic, even for automated vehicles. To fundamentally study and systematically resolve the tire blowout issue for automated vehicles, a collaborative project between General Motors (GM) and Arizona State University (ASU) has been conducted since 2018. In this dissertation, three main contributions of this project will be presented. First, to explore vehicle dynamics with tire blowout impacts and establish an effective simulation platform for close-loop control performance evaluation, high-fidelity tire blowout models are thoroughly developed by explicitly considering important vehicle parameters and variables. Second, since human cooperation is required to control Level 2/3 partially automated vehicles (PAVs), novel shared steering control schemes are specifically proposed for tire blowout to ensure safe vehicle stabilization via cooperative driving. Third, for Level 4/5 highly automated vehicles (HAVs) without human control, the development of control-oriented vehicle models, controllability study, and automatic control designs are performed based on impulsive differential systems (IDS) theories.
Co-simulations Matlab/Simulink® and CarSim® are conducted to validate performances of all models and control designs proposed in this dissertation. Moreover, a scaled test vehicle at ASU and a full-size test vehicle at GM are well instrumented for data collection and control implementation. Various tire blowout experiments for different scenarios are conducted for more rigorous validations. Consequently, the proposed high-fidelity tire blowout models can correctly and more accurately describe vehicle motions upon tire blowout. The developed shared steering control schemes for PAVs and automatic control designs for HAVs can effectively stabilize a vehicle to maintain path following performance in the driving lane after tire blowout.
In addition to new research findings and developments in this dissertation, a pending patent for tire blowout detection is also generated in the tire blowout project. The obtained research results have attracted interest from automotive manufacturers and could have a significant impact on driving safety enhancement for automated vehicles upon tire blowout.
ContributorsLi, Ao (Author) / Chen, Yan (Thesis advisor) / Berman, Spring (Committee member) / Kannan, Arunachala Mada (Committee member) / Liu, Yongming (Committee member) / Lin, Wen-Chiao (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2023
Description
Energy storage technologies are essential to overcome the temporal variability in renewable energy. The primary aim of this thesis is to develop reactor solutions to better analyze the potential of thermochemical energy storage (TCES) using non-stoichiometric metal oxides, for the multi-day energy storage application. A TCES system consists of a reduction reactor and an insulated MOx storage bin. The reduction reactor heats (to ~ 1100 °C) and partially reduces the MOx, thereby adding sensible and chemical energy (i.e., charging it) under reduced pO2 environments (~10 Pa). Inert gas removes the oxygen generated during reduction. The storage bin holds the hot and partially reduced MOx (typically particles) until it is used in an energy recovery device (i.e., discharge). Irrespective of the reactor heat source (here electrical), or the particle-inert gas flows (here countercurrent), the thermal reduction temperature and inert gas (here N2) flow minimize when the process approaches reversibility, i.e., operates near equilibrium. This study specifically focuses on developing a reduction reactor based on the theoretical considerations for approaching reversibility along the reaction path. The proposed Zigzag flow reactor (ZFR) is capable of thermally reducing CAM28 particles at temperatures ~ 1000 °C under an O2 partial pressure ~ 10 Pa. The associated analytical and numerical models analyze the reaction equilibrium under a real (discrete) reaction path and the mass transfer kinetic conditions necessary to approach equilibrium. The discrete equilibrium model minimizes the exergy destroyed in a practical reactor and identifies methods of maximizing the energy storage density () and the exergetic efficiency. The mass transfer model analyzes the O2 N2 concentration boundary layers to recommend sizing considerations to maximize the reactor power density. Two functional ZFR prototypes, the -ZFR and the -ZFR, establish the proof of concept and achieved a reduction extent, Δδ = 0.071 with CAM28 at T~950 °C and pO2 = 10 Pa, 7x higher than a previous attempt in the literature. The -ZFR consistently achieved > 100 Wh/kg during >10 h. runtime and the -ZFR displayed an improved = 130 Wh/kg during >5 h. operation with CAM28. A techno-economic model of a grid-scale ZFR with an associated storage bin analyzes the cost of scaling the ZFR for grid energy storage requirements. The scaled ZFR capital costs contribute < 1% to the levelized cost of thermochemical energy storage, which ranges from 5-20 ¢/kWh depending on the storage temperature and storage duration.
ContributorsGhotkar, Rhushikesh (Author) / Milcarek, Ryan (Thesis advisor) / Ermanoski, Ivan (Committee member) / Phelan, Patrick (Committee member) / Wang, Liping (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2023
Description
The conversion of H2S enables the recycling of a waste gas into a potential source of hydrogen at a lower thermodynamic energy cost as compared to water splitting. However, studies on the photocatalytic decomposition of H2S focus on traditional deployment of catalyst materials to facilitate this conversion, and operation only when a light source is available. In this study, the efficacy of Direct Ink Written (DIW) luminous structures for H2S conversion has been investigated, with the primary objective of sustaining H2S conversion when a light source has been terminated. Additionally, as a secondary objective, improving light distribution within monoliths for photocatalytic applications is desired. The intrinsic illumination of the 3D printed monoliths developed in this work could serve as an alternative to monolith systems that employ light transmitting fiber optic cables that have been previously proposed to improve light distribution in photocatalytic systems. The results that were obtained demonstrate that H2S favorable adsorbents, a wavelength compatible long afterglow phosphor, and a photocatalyst can form viscoelastic inks that are printable into DIW luminous monolithic contactors. Additionally, rheological, optical and porosity analyses conducted, provide design guidelines for future studies seeking to develop DIW luminous monoliths from compatible catalyst-phosphor pairs. The monoliths that were developed demonstrate not only improved conversion when exposed to light, but more significantly, extended H2S conversion from the afterglow of the monoliths when an external light source was removed. Lastly, considering growing interests in attaining a global circular economy, the techno-economic feasibility of a H2S-CO2 co-utilization plant leveraging hydrogen from H2S photocatalysis as a feed source for a downstream CO2 methanation plant has been assessed. The work provides preliminary information to guide future chemical kinetic design characteristics that are important to strive for if using H2S as a source of hydrogen in a CO2 methanation facility.
ContributorsAbdullahi, Adnan (Author) / Andino, Jean (Thesis advisor) / Phelan, Patrick (Thesis advisor) / Bhate, Dhruv (Committee member) / Wang, Robert (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023