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- All Subjects: Mechanical Engineering
Description
Concrete columns constitute the fundamental supports of buildings, bridges, and various other infrastructures, and their failure could lead to the collapse of the entire structure. As such, great effort goes into improving the fire resistance of such columns. In a time sensitive fire situation, a delay in the failure of critical load bearing structures can lead to an increase in time allowed for the evacuation of occupants, recovery of property, and access to the fire. Much work has been done in improving the structural performance of concrete including reducing column sizes and providing a safer structure. As a result, high-strength (HS) concrete has been developed to fulfill the needs of such improvements. HS concrete varies from normal-strength (NS) concrete in that it has a higher stiffness, lower permeability and larger durability. This, unfortunately, has resulted in poor performance under fire. The lower permeability allows for water vapor to build up causing HS concrete to suffer from explosive spalling under rapid heating. In addition, the coefficient of thermal expansion (CTE) of HS concrete is lower than that of NS concrete. In this study, the effects of introducing a region of crumb rubber concrete into a steel-reinforced concrete column were analyzed. The inclusion of crumb rubber concrete into a column will greatly increase the thermal resistivity of the overall column, leading to a reduction in core temperature as well as the rate at which the column is heated. Different cases were analyzed while varying the positioning of the crumb-rubber region to characterize the effect of position on the improvement of fire resistance. Computer simulated finite element analysis was used to calculate the temperature and strain distribution with time across the column's cross-sectional area with specific interest in the steel - concrete region. Of the several cases which were investigated, it was found that the improvement of time before failure ranged between 32 to 45 minutes.
ContributorsZiadeh, Bassam Mohammed (Author) / Phelan, Patrick (Thesis advisor) / Kaloush, Kamil (Thesis advisor) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2011
Description
Additively Manufactured Thin-wall Inconel 718 specimens commonly find application in heat exchangers and Thermal Protection Systems (TPS) for space vehicles. The wall thicknesses in applications for these components typically range between 0.03-2.5mm. Laser Powder Bed Fusion (PBF) Fatigue standards assume thickness over 5mm and consider Hot Isostatic Pressing (HIP) as conventional heat treatment. This study aims at investigating the dependence of High Cycle Fatigue (HCF) behavior on wall thickness and Hot Isostatic Pressing (HIP) for as-built Additively Manufactured Thin Wall Inconel 718 alloys. To address this aim, high cycle fatigue tests were performed on specimens of seven different thicknesses (0.3mm,0.35mm, 0.5mm, 0.75mm, 1mm, 1.5mm, and 2mm) using a Servohydraulic FatigueTesting Machine. Only half of the specimen underwent HIP, creating data for bothHIP and No-HIP specimens. Upon analyzing the collected data, it was noticed that the specimens that underwent HIP had similar fatigue behavior to that of sheet metal specimens. In addition, it was also noticed that the presence of Porosity in No-HIP specimens makes them more sensitive to changes in stress. A clear decrease in fatigue strength with the decrease in thickness was observed for all specimens.
ContributorsSaxena, Anushree (Author) / Bhate, Dhruv (Thesis advisor) / Liu, Yongming (Committee member) / Kwon, Beomjin (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
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
This dissertation is focused on the rheology scaling of metal particle reinforced polymermatrix composite made of solid and nanoporous metal powders to enable their
continuous 3D printing at high (>60vol%) metal content. There remained a specific
knowledge gap on how to predict successful extrusion with densely packed metals by
utilizing their suspension melt rheological properties. In the first project, the scaling of
the dynamic viscosity of melt-extrudate filaments made of Polylactic acid (PLA) and
gas-atomized solid NiCu powders was studied as a function of the metal’s volumetric
packing and feedstock pre-mixing strategies and correlated to its extrudability
performance, which fitted well with the Krieger-Dougherty analytical model. 63.4
vol% Filaments were produced by employing solution-mixing strategy to reduce
sintered part porosity and shrinkage. After sintering, the linear shrinkage dropped by
76% compared to the physical mixing. By characterizing metal particle reinforced
polymer matrix composite feedstock via flow-sweep rheology, a distinct extension of
shear-thinning towards high shear rates (i.e. 100 s-1) was observed at high metal content
– a result that was attributed to the improved wall adhesion. In comparison, physically mixed filament failed to sustain more than 10s-1 shear rate proving that they were prone
to wall slippage at a higher shear rate, giving an insight into the onset of extrusion
jamming. In the second project, nanoporous copper made out of electroless chemical
dealloying was utilized as fillers, because of their unique physiochemical properties.
The role of capillary imbibition of polymers into metal nanopores was investigated to
understand their effect on density, zero-shear viscosity, and shear thinning. It was
observed that, although the polymeric fluid’s transient concentration regulates its
wettability, the polymer chain length ultimately dictates its melt rheology, which consequentially facilitates densification of pores during vacuum annealing. Finally, it
was demonstrated that higher imbibition into nanopores leads to extrusion failure due
to a combined effect of volumetric packing increase and nanoconfinement, providing a
deterministic materials design tool to enable continuous 3D printing. The outcome of
this study might be beneficial to integrate nanoporous metals into binder-based 3D
printing technology to fabricate interdigitated battery electrodes and multifunctional 3D
printed electronics.
ContributorsHasib, Amm (Author) / Azeredo, Bruno (Thesis advisor) / Song, Kenan (Thesis advisor) / Nian, Qiong (Committee member) / Kwon, Beomjin (Committee member) / Li, Xiangjia (Committee member) / Arizona State University (Publisher)
Created2022
Description
For the past two centuries, coal has played a vital role as the primary carbon source, fueling industries and enabling the production of essential carbon-rich materials, including carbon nanotubes, graphite, and diamond. However, the global transition towards sustainable energy production has resulted in a decline in coal usage for energy purposes, with the United States alone witnessing a substantial 50% reduction over the past decade. This shift aligns with the UN’s 2030 sustainability goals, which emphasize the reduction of greenhouse gas emissions and the promotion of cleaner energy sources. Despite the decreased use in energy production, the abundance of coal has sparked interest in exploring its potential for other sustainable and valuable applications.In this context, Direct Ink Writing (DIW) has emerged as a promising additive manufacturing technique that employs liquid or gel-like resins to construct three-dimensional structures. DIW offers a unique advantage by allowing the incorporation of particulate reinforcements, which enhance the properties and functionalities of the materials. This study focuses on evaluating the viability of coal as a sustainable and cost-effective substitute for other carbon-based reinforcements, such as graphite or carbon nanotubes. The research utilizes a thermosetting resin based on phenol-formaldehyde (commercially known as Bakelite) as the matrix, while pulverized coal (250 µm) and carbon black (CB) function as the reinforcements. The DIW ink is meticulously formulated to exhibit shear-thinning behavior, facilitating uniform and continuous printing of structures. Mechanical property testing of the printed structures was conducted following ASTM standards. Interestingly, the study reveals that incorporating a 2 wt% concentration of coal in the resin yields the most significant improvements in tensile modulus and flexural strength, with enhancements of 35% and 12.5% respectively. These findings underscore the promising potential of coal as a sustainable and environmentally friendly reinforcement material in additive manufacturing applications. By harnessing the unique properties of coal, this research opens new avenues for its utilization in the pursuit of greener and more efficient manufacturing processes.
ContributorsSundaravadivelan, Barath (Author) / Song, Kenan (Thesis advisor) / Marvi, Hamidreza (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2023
Description
Ethylene is one of the most widely used organic compounds worldwide with ever increasing demand. Almost all the industries currently producing ethylene globally use the method of steam cracking, which, though highly selective and cost effective, is energy intensive along with having a high carbon footprint. This study aims to analyze micro-scale partial oxidation of propane as a novel approach towards ethylene generation which is simpler, less energy consuming, operates at lower temperatures and causes minimum CO2 emission. The experimental study endeavors to maximize the ethylene production by investigating the effect of variables such as temperature, flow rate, equivalence ratio and reactor diameter. The micro-scale partial oxidation of propane is studied inside quartz tube reactors of 1 mm and 3 mm diameter at a temperature range of 800 to 900 oC, at varying flow rates of 10 to 100 sccm and equivalence ratios of 1 to 6. The study reveals ethylene yield has a strong dependence on all the above factors. However, the factors are not completely independent of each other. Adjusting certain factors and levels results in greater ethylene yields as high as 10%, but propane to ethylene conversion efficiency is approximately constant for most conditions. Low CO2 concentrations are also recorded for most of the factor and level combinations, indicating the potential to achieve lower CO2 yields compared to conventional approaches. The investigation indicates promise for application in the field of ethylene generation.
ContributorsMAHALKAR, PAWAN MUKUND (Author) / Milcarek, Ryan (Thesis advisor) / Kwon, Beomjin (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2023
Description
Thermal management of electronics is critical to meet the increasing demand for high power and performance. Thermal interface materials (TIMs) play a key role in dissipating heat away from the microelectronic chip and hence are a crucial component in electronics cooling. Challenges persist with overcoming the interfacial boundary resistance and filler particle connectivity in TIMs to achieve thermal percolation while maintaining mechanical compliance. Gallium-based liquid metal (LM) capsules offer a unique set of thermal-mechanical characteristics that make them suitable candidates for high-performance TIM fillers. This dissertation research focuses on resolving the fundamental challenges posed by integration of LM fillers in polymer matrix. First, the rupture mechanics of LM capsules under pressure is identified as a key factor that dictates the thermal connectivity between LM-based fillers. This mechanism of oxide “popping” in LM particle beds independent of the matrix material provides insights in overcoming the particle-particle connectivity challenges. Second, the physical barrier introduced due to the polymer matrix needs to be overcome to achieve thermal percolation. Matrix fluid viscosity impacts thermal transport, with high viscosity uncured matrix inhibiting the thermal bridging of fillers. In addition, incorporation of solid metal co-fillers that react with LM fillers is adopted to facilitate popping of LM oxide in uncured polymer to overcome this matrix barrier. Solid silver metal additives are used to rupture the LM oxide, form inter-metallic alloy (IMC), and act as thermal anchors within the matrix. This results in the formation of numerous thermal percolation paths and hence enhances heat transport within the composite. Further, preserving this microstructure of interconnected multiphase filler system with thermally conductive percolation pathways in a cured polymer matrix is critical to designing high-performing TIM pads. Viscosity of the precursor polymer solution prior to curing plays a major role in the resulting thermal conductivity. A multipronged strategy is developed that synergistically combines reactive solid and liquid fillers, a polymer matrix with low pre-cure viscosity, and mechanical compression during thermal curing. The results of this dissertation aim to provide fundamental insights into the integration of LMs in polymer composites and give design knobs to develop high thermally conducting soft composites.
ContributorsUppal, Aastha (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Thesis advisor) / Kwon, Beomjin (Committee member) / Choksi, Gaurang (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2022
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
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