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- Genre: Academic theses
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
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (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
One of the fundamental aspects of cellular material design is cell shape selection. Of particular interest is how this selection can be made in the context of a realistic three-dimensional structure. Towards this goal, this work studied the stiffness response of periodic and stochastic lattice structures for the loading conditions of bending, torsion and tension/compression using commercially available lattice design optimization software. The goal of this computational study was to examine the feasibility of developing a ranking order based on minimum compliance or maximum stiffness for enabling cell selection. A study of stochastic shapes with different seeds was also performed. Experimental compression testing was also performed to validate a sample space of the simulations. The findings of this study suggest that under certain circumstances, stochastic shapes have the potential to generate the highest stiffness-to-weight ratio in the test environments considered.
ContributorsSharma, Raghav (Author) / Bhate, Dhruv (Thesis advisor) / Oswald, Jay (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Applications like integrated circuits, microelectromechanical devices, antennas, sensors, actuators, and metamaterials benefit from heterogeneous material systems made of metallic structures and polymer matrixes. Due to their distinctive shells made of metal and polymer, scaly-foot snails, which are found in the deep ocean, exhibit high strength and temperature resistance. Recent metal deposition fabrication techniques have been used to create a variety of multi-material structures. However, using these complex hybrid processes, it is difficult to build complex 3D structures of heterogeneous material with improved properties, high resolution, and time efficiency. The use of electrical field-assisted heterogeneous material printing (EFA-HMP) technology has shown potential in fabricating metal-composite materials with improved mechanical properties and controlled microstructures. The technology is an advanced form of 3D printing that allows for printing multiple materials with different properties in a single print. This allows for the creation of complex and functional structures that are not possible with traditional 3D printing methods. The development of a photocurable printing solution was carried out that can serve as an electrolyte for charge transfer and further research into the printing solution's curing properties was conducted. A fundamental understanding of the formation mechanism of metallic structures on the polymer matrix was investigated through physics-based multiscale modeling and simulations. The relationship between the metallic structure's morphology, the printing solution's properties, and the printing process parameters was discovered.The thesis aims to investigate the microstructures and electrical properties of metal-composite materials fabricated using EFA-HMP technology and to evaluate the correlation between them. Several samples of metal-composite materials with different microstructures will be fabricated using EFA-HMP technology to accomplish this. The results of this study will provide a better understanding of the relationship between the microstructures and properties of metal-composite materials fabricated using EFA-HMP technology and contribute to the development of new and improved materials in various fields of application. Furthermore, this research will also shed light on the advantages and limitations of EFA-HMP technology in fabricating metal-composite materials and study the correlation between the microstructures and mechanical properties.
ContributorsTiwari, Lakshya (Author) / Li, Xiangjia (Thesis advisor) / Yang, Sui (Committee member) / Mu, Linqin (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Progressive miniaturization in electronics demands advanced materials with excellent energy conversion and transport properties. Opportunities exist in novel material morphologies such as hierarchical structures, multi-functional composites and nanoscale architectures which may offer mechanical, thermal and electronic properties tailored to a wide range of applications (e.g., aerospace, robotics, biomedical etc.). However, the manufacturing capabilities have always posed a grand challenge in realizing the advanced material morphologies. Furthermore, the multi-scale modeling of complex material architectures has been extremely challenging owing to the limitations in computation methodologies and lack of understanding in nano-/micro-meter scale physics. To address these challenges, this work considers the morphology effect on carbon nanotube (CNT)-based composites, CNT fibers and thermoelectric (TE) materials. First, this work reports additively manufacturable TE morphologies and analyzes the thermo-electric transport behavior. This research introduces innovative honeycomb TE architectures that showed ~26% efficiency increase and ~25% density reduction compared to conventional rectangular TE architectures. Moreover, this work presents 3D printable compositionally segmented TE architecture which provides record-high efficiencies (up to 8.7%) over wide temperature ranges if the composition and aspect ratio of multiple TE materials are optimized within a single TE device. Next, this research proposes computationally efficient two-dimensional (2D) finite element model (FEM) to study the electrical and thermal properties in CNT based composites by simultaneously considering the stochastic CNT distributions, CNT fractions (upto 80%) and interfacial resistances. The FEM allows to estimate the theoretical maximum possible conductivities with corresponding interfacial resistances if the CNT morphologies are carefully controlled, along with appreciable insight into the energy transport physics. Then, this work proposes a data-driven surrogate model based on convolutional neural networks to rapidly approximate the composite conductivities in a second with accuracy > 98%, compared to FEM taking >100 minutes per simulation. Finally, this research presents a pseudo 2D FEM to approximate the electrical and thermal properties in CNT fibers at various CNT aspect ratios (up to 10,000) by simultaneously considering CNT-CNT interfacial effects along with the stochastic distribution of inter-bundle voids.
ContributorsEjaz, Faizan (Author) / Kwon, Beomjin (Thesis advisor) / Zhuang, Houlong (Committee member) / Song, Kenan (Committee member) / Wang, Robert (Committee member) / Kang, Wonmo (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
Description
Nanomaterials redefine the lens through which the world is viewed today. The miniaturization of devices and systems to the nanoscale explodes the realm of what is possible as the interactions with neighboring atoms and molecules increase. This interactivity creates ripple effects that lead to superior mechanical, thermal, electrical, and optical properties that are highly desired across several industries. Two-dimensional (2D) materials are a branch of this family, and the focus of this paper revolves around a recent addition to this category called MXenes. The versatile properties of these 2D nanomaterials have made them unique, as they have the desired performance that can be utilized in several industries, especially energy management, wastewater treatment, and microelectronic devices. Followed by the MAX phase synthesis, hydrofluoric (HF) acid has been the primary etchant utilized to derive these 2D nanoparticles. However, alternative etchants via reactions are desirable to achieve similar selective etching without involving highly toxic HF. Therefore, this study investigated MXene synthesis and applications in 3D printing, followed by the formation of the precursor MAX, an optimized in-situ etching method, and streamlined post-etching processes to maximize 2D MXene yield. The etched powders were then analyzed using scanning electron microscopy (SEM), x-ray diffraction (XRD), atomic force microscopy (AFM), and energy-dispersive x-ray spectroscopy (EDS) characterization methods to verify and validate the MXene dimensions, chemistry, and crystal structures. Simple applications, such as the dispersion feasibility for customizing micropatterns via 3D printing, were also demonstrated as examples. Finally, this research showed the simple processing of 2D MXenes and their potential in structural support, heat dissipation, microelectronics, optical meta-surfaces, and other areas.
ContributorsFagade, Mofetoluwa (Author) / Song, Kenan (Thesis advisor) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2023