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.

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.

Polymer composite has been under rapid development with advancements in polymer chemistry, synthetic fibers, and nanoparticles. With advantages such as lightweight, corrosion resistance, and tunable functionalities, polymer composite plays a significant role in various applications such as aerospace, wearable electronics, energy storage systems, robotics, biomedicine, and microelectronics. In general, polymer composite can be divided into particulate-filled, fiber-filled, or network-filled types depending on the manufacturing process and internal structure. Over the years, fabrication processes on the macro- and micro-scales have been extensively explored. For example, lamination, fiber tow steering, and fiber spinning correspond to meter, millimeter, and micrometer scales, respectively. With the development of nanoparticles and their exceptional material properties, polymer nanoparticle composite has shown promising material property enhancements. However, the lack of economical solutions to achieve nanoscale nanoparticle morphology control limits the reinforcement efficiency and industrial applications. This dissertation focuses on utilizing additive manufacturing as a tooling method to achieve nanoparticle morphology control in polymer nanocomposite fibers. Chapter 1 gives a thorough background review regarding fiber composite, additive manufacturing, and the importance of nanoparticle orientation. Two types of nozzle designs, concentrical and layer-by-layer, are 3D printed and combined with the dry-jet-wet fiber spinning method to create continuous fibers with internal structures. Chapters 2 to 5 correspond to four stages of my research, namely, (2) multi-material fiber spinning, (3) interfacial-assisted nanoparticle alignment, (4) microscale patterning, and (5) nanoscale patterning. The achieved feature resolution also improves from 100 µm, 10 µm, 2 µm, to 170 nm, respectively. The process-structural-property relationship of polymer nanocomposite fibers is also investigated with applications demonstrations including sensors, electrically conductive fibers, thermally conductive fibers, and mechanically reinforced fibers. At last, Chapter 6 gives a summary and some future perspectives regarding fiber composites.

The colloidal solutions of nanoparticles have been seen as promising solutions forheat transfer enhancement. Additionally, there has been an accelerated study on the effects of ultrasound on heat transfer enhancement in recent years. A few authors have studied the combined impact of Al2O3 nanofluids and ultrasound on mini channels. This study focused on the combined effects of Al2O3 nanofluids and ultrasound on heat transfer enhancement in a circular mini channel heat sink. Two concentrations of Al2O3-water nanofluids, i.e., 0.5% and 1%, were used for the experiments in addition to two heat input conditions, namely 40 W and 50 W providing a constant heat flux of 25000 W m-2 and 31250 W m-2 respectively. The effect on the nanofluids using 5 W ultrasound was analyzed. Experimental observations show that the usage of ultrasound increased the heat transfer coefficient. The heat transfer coefficient also increased with increasing nanoparticle concentration and high heat flux. The average heat transfer coefficient enhancement for 0.5% and 1% nanofluid due to increased heat flux in the absence of ultrasound was 12.4% and 9% respectively. At a constant heat input of 40 W, the induction of ultrasound enhanced the heat transfer coefficient by 22.8% and 23.9% for 0.5% and 1% nanofluid respectively. Similarly, for a constant heat input of 50 W, the usage of ultrasound enhanced the heat transfer coefficient by 19.8% and 22.9% for 0.5% and 1% nanofluid respectively Also, interesting findings are reported with low heat input with ultrasound vs. high heat input without ultrasound (i.e., 40 W with US vs. 50 W without US). The heat transfer coefficient and Nusselt number for 0.5% and 1% concentrations was enhanced by 9.2% and 13.6%, respectively. Furthermore, for fixed heat input powers of 40 W and 50 W, increasing the concentration from 0.5% to 1% along with ultrasound yielded an average enhancement in Nu of 38.3% and 32.4% respectively

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.

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.

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.

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.

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.