Matching Items (32)
Description
This paper discusses the theoretical approximation and attempted measurement of the quantum <br/>force produced by material interactions though the use of a tuning fork-based atomic force microscopy <br/>device. This device was built and orientated specifically for the measurement of the Casimir force as a <br/>function of separation distance using a piezo actuator for approaching and a micro tuning fork for the <br/>force measurement. This project proceeds with an experimental measurement of the ambient Casmir force <br/>through the use of a tuning fork-based AFM to determine its viability in measuring the magnitude of the <br/>force interaction between an interface material and the tuning fork probe. The ambient measurements <br/>taken during the device’s development displayed results consistent with theoretical approximations, while<br/>demonstrating the capability to perform high-precision force measurements. The experimental results<br/>concluded in a successful development of a device which has the potential to measure forces of <br/>magnitude 10−6 to 10−9 at nanometric gaps. To conclude, a path to material analysis using an approach <br/>stage, alternative methods of testing, and potential future experiments are speculated upon.
ContributorsMulkern, William Michael (Author) / Wang, Liping (Thesis director) / Kwon, Beomjin (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
Ultrasound has become one of the most popular non-destructive characterization tools for soft materials. Compared to conventional ultrasound imaging, quantitative ultrasound has the potential of analyzing detailed microstructural variation through spectral analysis. Because of having a better axial and lateral resolution, and high attenuation coefficient, quantitative high-frequency ultrasound analysis (HFUA) is a very effective tool for small-scale penetration depth application. One of the QUS parameters, peak density had recently shown a promising response with the variation in the soft material microstructure. Acoustic scattering is arguably the most important factor behind different parametric responses in ultrasound spectra. Therefore, to evaluate peak density, acoustic scattering at different frequency levels was investigated. Analytical, computational, and experimental analysis was conducted to observe both single and multiple scattering in different microstructural setups. It was observed that peak density was an effective tool to express different levels of acoustic scattering that occurred through microstructural variation. The feasibility of the peak density parameter was further evaluated in ultrasound C-scan imaging. The study was also extended to detect the relative position of the imaged structure in the direction of wave propagation. For this purpose, a derivative parameter of peak density named mean peak to valley distance (MPVD) was developed to address the limitations of peak density. The study was then focused on detecting soft tissue malignancy. The histology-based computational study of HFUA was conducted to detect various breast tumor (soft tissue) grades. It was observed that both peak density and MPVD parameters could identify tumor grades at a certain level. Finally, the study was focused on evaluating the feasibility of ultrasound parameters to detect asymptotic breast carcinoma i.e., ductal carcinoma in situ (DCIS) in the surgical margin of the breast tumor. In that computational study, breast pathologies were modeled by including all the phases of DCIS. From the similar analysis mentioned above, it was understood that both peak density and MPVD parameters could detect various breast pathologies like ductal hyperplasia, DCIS, and calcification during intraoperative margin analysis. Furthermore, the spectral features of the frequency spectrums from various pathologies also provided significant information to identify them conclusively.
ContributorsPaul, Koushik (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Holloway, Julianne (Committee member) / Li, Xiangjia (Committee member) / Liu, Yongming (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
With increasing advance complexity in the structure to be 3D printed, the use of post processing removal of support structures has become more complicated thing due to the need of newer tool case to remove supports in such scenarios. Attempts have been made to study, research and experiment the dissolvable and recyclable photo-initiated polymeric resin that can be used to build support structure. Vat photo-polymerization method of manufacturing was selected due to wide range of materials that can be selected and researched which can have the potential to be selected further for large scale manufacturing. Deep understanding of the recyclable polymer was done by performing chemical and mechanical property test. Varying light intensities are used to study the curing properties and respective dissolving properties. In this thesis document, recyclable and dissolvable polymeric resin have been selected to print the support structures which can be later dissolved and recycled.The resin was exposed to varying light projections using grayscales of 255, 200 and 150 showing different dissolving time of each structure. Dissolving time of the printed parts were studied by varying the surface to volume ratios of the part. Higher the surface to volume ratios of the printed part resulted in lower time it takes to dissolve the part in the dissolving solution. The mechanical strengths of the recycled part were found to be pretty solid as compared to the freshly prepared resin, good sign of using it for multiple times without degrading its strength. Cactus shaped model was printed using commercial red resin and supports with the recyclable solution to deeply understand the working and dissolving properties of recyclable resin. Without any external efforts, the supports were easily dissolved in the solution, leaving the cactus intact. Further work is carried on printing Meta shaped gyroid lattice structure in effort to lower the dissolving time of the supports while maintaining enough mechanical stress. Future efforts will be made to conduct the rheology test and further lower the dissolving time as much it can to be ready for the commercial large scale applications.
ContributorsNawab, Prem Kalpesh (Author) / Li, Xiangjia (Thesis advisor) / Zhuang, Houlong (Committee member) / Jin, Kailong (Committee member) / Arizona State University (Publisher)
Created2023
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
This paper explores to mitigate the issue of Formula SAE brakes vaporizing by creating a computational model to determine when the fluid may boil given a velocity profile and brake geometry. The paper explores various parameters and assumptions and how they may lead to error determining when the brake fluid will vaporize. Common assumptions such as a constant convection coefficient are questioned throughout the paper and compared to methods requiring higher computational power. Throughout this model, a significant dependence on the heat partition factor is found on the final steady state temperature of the brake fluid is found, and a sensitivity analysis is performed to determine the effect of its variation.
ContributorsWesterhoff, Andrew (Author) / Kwon, Beomjin (Thesis director) / Milcarek, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-05
Description
Stereolithography (SLA) is an innovative additive manufacturing technique that has gained immense popularity in recent times due to its ability to produce complex and precise three-dimensional objects. However, the quality of the final product depends on the stability and homogeneity of the photocurable metallic ink used, which is crucial for manufacturing high-quality parts with good surface finish and higher density. To achieve homogeneity in the photocurable metallic resin, the study conducted on optimizing the printing ink for ultrafast layer less fabrication of 3D metal objects investigated the effectiveness of different dispersants such as KH 560, Triton X-100, BYK 2013, BYK 2030, and BYK 111. The use of dispersants plays a vital role in optimizing the ink and enhancing the surface finish and density of the final product. The rheology results showed that the appropriate dispersant has the potential to improve the properties of the printing ink and benefit the integrity of the printed green bodies and their surface finish. By using the optimized suspension, the study was able to fabricate parts with high metallic loading at an ultrafast speed using the Continuous Liquid Interface Production technique. FTIR analysis, sedimentation testing, and rheology study has been carried out which demonstrates the effects of the utilization of various dispersants optimally to improve the homogeneity and manufactured part’s integrity. Power law has been used to understand the viscosity behavior of dispersants in a photocurable ink with copper sulfate keeping the parameters such as shearing rate, stress, and torque intact. The microscopic images of the sintered parts showed high precision and an extremely smooth surface finish, which underscores the technique's potential to produce high-quality 3D metal objects. The solubility of dispersants significantly influenced the structural quality after washing and debinding processes. This study provides valuable information to design photocurable metallic suspensions for metal salts like copper sulfate pentahydrate.
ContributorsVerma, Harsh Pyarelal (Author) / Li, Xiangjia (Thesis advisor) / Nian, Qiong (Committee member) / Xie, Renxuan (Committee member) / Arizona State University (Publisher)
Created2023