Matching Items (72)
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- All Subjects: Mechanical Engineering
- Creators: Phelan, Patrick
- Member of: ASU Electronic Theses and Dissertations
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
A Compact Linear Fresnel Reflector (CLFR) is a simple, cost-effective, and scalable option for generating solar power by concentrating the sun rays. To make a most feasible application, design parameters of the CLFR, such as solar concentrator design parameters, receiver design parameters, heat transfer, power block parameters, etc., should be optimized to achieve optimum efficiency. Many researchers have carried out modeling and optimization of CLFR with various numerical or analytical methods. However, often computational time and cost are significant in these existing approaches. This research attempts to address this issue by proposing a novel computational approach with the help of increased computational efficiency and machine learning. The approach consists of two parts: the algorithm and the machine learning model. The algorithm has been created to fulfill the requirement of the Monte Carlo Ray tracing method for CLFR collector simulation, which is a simplified version of the conventional ray-tracing method. For various configurations of the CLFR system, optical losses and optical efficiency are calculated by employing these design parameters, such as the number of mirrors, mirror length, mirror width, space between adjacent mirrors, and orientation angle of the CLFR system. Further, to reduce the computational time, a machine learning method is used to predict the optical efficiency for the various configurations of the CLFR system. This entire method is validated using an existing approach (SolTrace) for the optical losses and optical efficiency of a CLFR system. It is observed that the program requires 6.63 CPU-hours of computational time are required by the program to calculate efficiency. In contrast, the novel machine learning approach took only seconds to predict the optical efficiency with great accuracy. Therefore, this method can be used to optimize a CLFR system based on the location and land configuration with reduced computational time. This will be beneficial for CLFR to be a potential candidate for concentrating solar power option.
ContributorsLunagariya, Shyam (Author) / Phelan, Patrick (Thesis advisor) / Kwon, Beomjin (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cellular metamaterials arouse broad scientific interests due to the combination of host material and structure together to achieve a wide range of physical properties rarely found in nature. Stochastic foam as one subset has been considered as a competitive candidate for versatile applications including heat exchangers, battery electrodes, automotive, catalyst devices, magnetic shielding, etc. For the engineering of the cellular foam architectures, closed-form models that can be used to predict the mechanical and thermal properties of foams are highly desired especially for the recently developed ultralight weight shellular architectures. Herein, for the first time, a novel packing three-dimensional (3D) hollow pentagonal dodecahedron (HPD) model is proposed to simulate the cellular architecture with hollow struts. An electrochemical deposition process is utilized to manufacture the metallic hollow foam architecture. Mechanical and thermal testing of the as-manufactured foams are carried out to compare with the HPD model. Timoshenko beam theory is utilized to verify and explain the derived power coefficient relation. Our HPD model is proved to accurately capture both the topology and the physical properties of hollow stochastic foam. Understanding how the novel HPD model packing helps break the conventional impression that 3D pentagonal topology cannot fulfill the space as a representative volume element. Moreover, the developed HPD model can predict the mechanical and thermal properties of the manufactured hollow metallic foams and elucidating of how the inevitable manufacturing defects affect the physical properties of the hollow metallic foams. Despite of the macro-scale stochastic foam architecture, nano gradient gyroid lattices are studied using Molecular Dynamics (MD) simulation. The simulation result reveals that, unlike homogeneous architecture, gradient gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. The deformation behavior and energy absorption are predictable and designable, which demonstrate its highly programmable potential.
ContributorsDai, Rui (Author) / Nian, Qiong (Thesis advisor) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Liu, Yongming (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2021
Description
Concentrating solar thermal power systems gained a wide interest for a long time to serve as a renewable and sustainable alternate source of energy. While the optimization and modification are ongoing, focused generally on solar power systems to provide solar-electrical energy or solar-thermal energy, the production process of Ordinary Portland Cement (OPC) has not changed over the past century. A linear refractive Fresnel lens application in cement production process is investigated in this research to provide the thermal power required to raise the temperature of lime up to 623 K (350C) with zero carbon emissions for stage two in a new proposed two-stage production process. The location is considered to be Phoenix, Arizona, with a linear refractive Fresnel lens facing south, tilted 33.45 equaling the location latitude, and concentrating solar beam radiation on an evacuated tube collector with tracking system continuously rotating about the north-south axis. The mathematical analysis showed promising results based on averaged monthly values representing an average hourly useful thermal power and receiver temperature during day-light hours for each month throughout the year. The maximum average hourly useful thermal power throughout the year was obtained for June as 33 kWth m-2 with a maximum receiver temperature achieved of 786 K (513C), and the minimum useful thermal power obtained during the month of December with 27 kWth m-2 and a minimum receiver temperature of 701 K (428C).
ContributorsAlkhuwaiteem, Mohammad (Author) / Phelan, Patrick (Thesis advisor) / Shuaib, Abdelrahman (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
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
Description
Sweat evaporation is fundamental to human thermoregulation, yet our knowledge of the microscale sweat droplet evaporation dynamics is very limited. To study sweat droplet evaporation, a reliable way to measure sweat evaporation rate from skin and simultaneously image the droplet dynamics through midwave infrared thermography (MWIR) or optical coherence tomography (OCT) is required. Ventilated capsule is a common device employed for measuring sweat evaporation rates in physiological studies. However, existing designs of ventilated capsules with cylindrical flow chambers create unrealistic flow conditions that include flow separation and swirling. To address this problem, this thesis introduces a ventilated capsule with rectangular sweat evaporation area preceded by a diffuser section with geometry based on wind tunnel design guidelines. To allow for OCT or MWIR imaging, a provision to install an acrylic or a sapphire window directly over the exposed skin surface being measured is incorporated in the design. In addition to the capsule, a simplified artificial sweating surface that can supply water in a filmwise, single or multiple droplet form was developed. The performance of the capsule is demonstrated using the artificial sweating surface along with example MWIR imaging.
ContributorsRamesh, Rajesh (Author) / Rykaczewski, Konrad (Thesis advisor) / Kavouras, Stavros (Committee member) / Phelan, Patrick (Committee member) / Burke, Richard (Committee member) / Arizona State University (Publisher)
Created2023
Description
The space industry is rapidly expanding, and components are getting increasinglysmaller leading to the prominence of cubesats. Cubesats are satellites from about coffee
mug size to cereal box size. The challenges of shortened timeline and smaller budgets for
smaller spacecraft are also their biggest advantages. This benefits educational missions
and industry missions a like but can burden teams to be smaller or have less experience.
Thermal analysis of cubesats is no exception to these burdens which is why this thesis
has been written to provide a guide for conducting the thermal analysis of a cubesat using
the Deployable Optical Receiver Aperture (DORA) mission as an example. Background
on cubesats and their role in the space industry will be examined. The theoretical side of
heat transfer necessary for conducting a thermal analysis will be explored. The DORA
thermal analysis will then be conducted by constructing a thermal model in Thermal
Desktop software from the ground up. Insight to assumptions for model construction to
move accurately yet quickly will be detailed. Lastly, this fast and quick method will be
compared to a standard finite element mesh model to show quality results can be
achieved in significantly less time.
ContributorsAdkins, Matthew Thomas (Author) / Phelan, Patrick (Thesis advisor) / Jacobs, Danny (Thesis advisor) / Wang, Liping (Committee member) / Bowman, Judd (Committee member) / Arizona State University (Publisher)
Created2022
Description
This research aims to develop a single-phase immersion cooling system for CPU (Central Processing Unit) processors. To achieve this, a heat pipe with a
dielectric liquid is designed to be used to cool the CPU, relying only on natural
convection. A Tesla valve phenomenon is used to achieve the one-directional,
recirculating system. A comparative study was conducted between two different
single-phase dielectric fluids Mineral Oil and FC 3283 (Fluorocarbon), utilizing
natural convection and Boussinesq correlations. ANSYS Fluent was used to conduct
CFD (Computational Fluid Dynamics) analysis, demonstrating natural convection
and recirculating flow in the heating direction. A comparison was made between the
traditional cooling method of air and the developed immersion cooling system, with
the results indicating that the system is capable of reducing the operating
temperature of the CPU by 40 to 50 degrees Celsius, depending on the power
consumption. The results of the experiment conducted showed that a processor cooled
by Mineral oil would operate at 56 degrees Celsius, while a processor cooled by FC
3283 would operate at 47 degrees Celsius. By comparison, a processor cooled by the
traditional air-cooled system would operate between 80 and 100 degrees Celsius.
These results demonstrate that the Mineral oil and FC 3283 cooling systems are
significantly more efficient than the traditional air-cooled system. This could prove to
be a valuable asset in the development of more efficient cooling systems. Further
research is necessary to evaluate the longevity, cost-effectiveness, and benefits of
these systems in comparison to traditional air cooling
ContributorsGajjar, Kathan Malaybhai (Author) / Huang, Huei Ping (Thesis advisor) / Chen, Kangping (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2023
Description
Solid Oxide Fuel Cells (SOFCs) generate electricity using only hydrogen and oxygen and they form H2O as the only byproduct, giving them the potential to significantly reduce carbon emissions and the impacts of global warming. In order to meet the global power demands today, SOFCs need to significantly increase their power density and improve robustness in startup and cycling operations. This study explores the impact of decreasing the anode thickness to improve the mass transport of the fuel through the anode of a micro-tubular (mT) SOFC because few studies have reported the correlation between the two. Decreasing the thickness decreases the chance for concentration overpotential which is caused by not enough of the reactants being able to reach the reaction site while products are not able to be removed quickly enough. Experiments were performed in a split tube furnace heated to 750°C with nickel-yttria stabilized zirconia (Ni-YSZ) supported cells. Pure hydrogen was supplied to the cell at rates of 10, 20, 30, and 40 mL/min while the cathode was supplied air from the environment. The cell's performance was studied using the current-voltage method to generate polarization curves and electrochemical impedance spectroscopy to create Bode and Nyquist plots. The results from the electrochemical impedance spectroscopy show a lower impedance for the frequencies pertaining to the gas diffusion in the anode for the thinner cells. This suggests that decreasing the anode thickness increases the mass transport of the gas. Additionally, through a distribution of relaxation times (DRT) analysis, the peaks vary between the two cell thicknesses at the frequencies pertaining to gas diffusion in anode-supported cells, implicating the decreased resistance created by thinning the anode layer.
ContributorsPhillips, Kristina (Author) / Milcarek, Ryan (Thesis advisor) / Wang, Robert (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2023
Description
Gallium based room-temperature liquid metals (LMs) have special properties such as metal-like high thermal conductivity while in the liquid state. They are suitable for many potential applications, including thermal interface materials, soft robotics, stretchable electronics, and biomedicine. However, their high density, high surface tension, high reactivity with other metals, and rapid oxidation restrict their applicability. This dissertation introduces two new types of materials, LM foams, and LM emulsions, that address many of these issues. The formation mechanisms, thermophysical properties, and example applications of the LM foams and emulsions are investigated.LM foams can be prepared by shear mixing the bulk LM in air using an impeller. The surface oxide layer is sheared and internalized into the bulk LM as crumpled oxide flakes during this process. After a critical amount of oxide flakes is internalized, they start to stabilize air bubbles by encapsulating and oxide-bridging. This mechanism enables the fabrication of a LM foam with improved properties and better spreadability.
LM emulsions can be prepared by mixing the LM foam with a secondary liquid such as silicone oil (SO). By tuning a few factors such as viscosity of the secondary liquid, composition, and mixing duration, the thermophysical properties of the emulsion can be controlled. These emulsions have a lower density, better spreadability, and unlike the original LM and LM foam, they do not induce corrosion of other metals.
LM emulsions can form by two possible mechanisms, first by the secondary liquid replacing air features in the existing foam pores (replacement mechanism) and second by creating additional liquid features within the LM foam (addition mechanism). The latter mechanism requires significant oxide growth and therefore requires presence of oxygen in the environment. The dominant mechanism can therefore be distinguished by mixing LM foam with the SO in air and oxygen-free environments. Additionally, a comprehensive analysis of foam-to-emulsion density change, multiscale imaging and surface wettability confirm that addition mechanism dominates the emulsion formation. These results provide insight into fundamental processes underlying LM foams and emulsions, and they set up a foundation for preparing LM emulsions with a wide range of fluids and controllable properties.
ContributorsShah, Najam Ul Hassan (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Thesis advisor) / Phelan, Patrick (Committee member) / Green, Matthew D. (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023