<p>This study comprises experimental investigations and theoretical/empirical analyses of three forced-convective (pumped) boiling schemes: (i) confined round single jet and jet array impingement boiling, and flow boiling through conventional-sized (ii) concentric circular annuli and (iii) rectangular channels. These schemes could be utilized in the thermal management of various applications including high-heat-flux electronic devices, power devices, electric vehicle charging cables, avionics, future space vehicles, etc.</p>

<p>  </p> <p>Part I studies the lubricant sprays and droplets in a rotary compressor. Air conditioning (AC) systems are now widely used in residential and commercial environments, while the compressor is the most important element in the AC system, and rotary compressors are often used in split AC appliances, whose number is estimated to reach 3.7 billion in 2050. In a rotary compressor, the lubricant oil atomizes into small droplets due to the differential pressure in and out of the cylinder. Part of the lubricant oil droplets carried by the refrigerant vapor will ultimately exhaust from the compressor through the discharge pipe. The ratio of the discharged oil volume to the total oil volume is characterized as the Oil Discharge Ratio (ODR). High ODR will reduce the reliability of the compressor and deteriorate the heat transfer of the condenser and the evaporator, resulting in decreased efficiency. Thus, controlling the ODR is a key issue for the design of the rotary compressor.</p> <p>In Part I, rotary compressors were modified to provide optical access into its internal space, i.e., the lower cavity (refers to the space between the cylinder and the motor), above the rotor/stator, and at the discharge tube level. The modified rotary compressors’ operation was supported by a test rig which provided a wide range of operating conditions, e.g., pressure and frequency. Thus, in-situ optical measurements, e.g., shadowgraph and holograph, can be performed to visualize the lubricant sprays and droplets in the rotary compressor. An image processing routine containing the Canny operator and Convolutional Neural-Network was developed to identify droplets from high-resolution shadowgraph images, while Particle Image Velocimetry (PIV) and Optical Flow Velocimetry (OFV) were applied to calculate the spray and droplet’s velocities with time-resolved shadowgraph images. Parallel Four-Step Phase Shifting Holograph (PFSPSH) located the droplets’ positions in a three-dimensional volume under the specific operating condition.</p> <p>Both primary and secondary atomization were observed in the rotary compressor, while primary atomization is the major source of droplet production. The droplet size distributions versus the frequency, vertical direction, radial direction, and pressure are obtained. It is observed that the droplet characteristic mean diameters increase with the frequency and pressure. They also become larger in the outer region above the rotor/stator and keep constant in the radial direction at the discharge tube level. The penetration velocity of the lubricant spray is calculated in the lower cavity. An outward shift of the jet core combined with an outward velocity component was observed. Additionally, horizontal swirling velocity above the rotor/stator and at the discharge tube level and the vertical recirculation velocity above the rotor/stator are characterized. The volume fraction of droplets was also characterized under the specific operating condition. The results provide detailed experimental data to set up the boundary conditions used in CFD. They also show that the droplets in the upper cavity are mostly from the discharge process of the cylinder in the lower cavity. The results support a droplet pathway model in the rotary compressor, which can guide the optimization of future rotary compressors.</p> <p>Evaporation is commonly seen in hydrology, agriculture, combustion, refrigeration, welding, etc. And it always accompanies heat and mass transfer at the liquid-gas interface and is affected by the substance’s properties, the environment’s pressure, temperature, convection, and so on. PFSPSH in Part I aims to retrieve the phase information for holograph reconstruction. Part II further explores the application of the PFSPSH technology in Part I to observe the evaporation process of acetone, where the phase disturbance caused by the vapor is used to reconstruct the vapor concentration in space. The method is called Parallel Four-Step Phase Shifting Interferometer (PFSPSI). The first case studies the evaporation process of the acetone contained in a liquid pool with uniform air flow in a low-speed wind tunnel. The mole fractions of the acetone vapor near the liquid-air interface with different air speeds are characterized. The second case studies the evaporation process of acetone droplets levitated by an ultrasound levitator. The mole fraction of the acetone vapor near the liquid-air interface is characterized by assuming an axisymmetric field and using the analytical solution of the inverse Abel transform. The asymmetric pattern of the acetone vapor field is observed, which is considered due to the drastic sound pressure change at the stand wave location produced by the ultrasound levitator. The mass transfer of the evaporation process by the vapor’s mole fraction is calculated and compared with the mass transfer calculated by the droplet size change. It is observed that the mass transfer by the vapor’s mole fraction is generally smaller than the mass transfer calculated by the droplet size change, which can be explained by the convection process induced by the acoustic streaming.</p>

This thesis presents a modelling approach of laser welding process of aluminium alloy from the thermo-mechanical point of view to evaluate the occurrence of hot cracking based on simulation results and relevant criteria. The model was created stepwise in COMSOL Multiphysics, starting with the thermal model where heat conduction of solid and liquid phase was computed. Then the CFD model was created by involving the driving forces of liquid motion in the weld pool, i.e. natural convection and Marangoni effect. Lastly, the temperature profile calculated by the CFD model was loaded into the mechanical model for computation of thermal stress and strain. The mechanical results were required in  criteria for measuring the  susceptibility of hot cracking. The main findings include that Marangoni effect plays a dominant role in generating the fluid flow and convective heat flux in the weld pool, thus enhancing the heat dissipation and lowering temperature in the workpiece. By contrast, such temperature reduction caused by the air convection, radiation and natural convection is negligible. The welding track further from the clamped side experiences smaller transversal residual stress, but it does not necessarily suggest higher susceptibility to hot cracking according to the applied criteria. It can be concluded judging from current results that these first models of laser welding process work satisfactorily. There is still a work to do to obtain the full maturity of this model due to its limitation and some assumptions made for simplicity. / Denna avhandling presenterar en modelleringsmetod för lasersvetsningsprocessen av aluminiumlegering ur termomekanisk synvinkel för att utvärdera förekomsten av het sprickbildning baserat på simuleringsresultat och relevanta kriterier. Modellen skapades stegvis i COMSOL Multiphysics, med början med den termiska modellen där värmeledning av fast och flytande fas beräknades. Sedan skapades CFD-modellen genom att involvera drivkrafterna för flytande rörelse i svetsbassängen, dvs. naturlig konvektion och Marangoni-effekt. Slutligen laddades temperaturprofilen beräknad av CFD-modellen in i den mekaniska modellen för beräkning av termisk stress och töjning. De mekaniska resultaten krävdes i kriterier för att mäta känsligheten för het sprickbildning. De viktigaste resultaten inkluderar att Marangoni-effekten spelar en dominerande roll när det gäller att generera vätskeflödet och konvektivt värmeflöde i svetsbassängen, vilket förbättrar värmeavledningen och sänker temperaturen i arbetsstycket. Däremot är sådan temperaturreduktion orsakad av luftkonvektion, strålning och naturlig konvektion försumbar. Svetsbanan längre från den fastspända sidan upplever mindre tvärgående restspänning, men det föreslår inte nödvändigtvis högre känslighet för hetsprickning enligt de tillämpade kriterierna. Man kan dra slutsatsen utifrån aktuella resultat att dessa första modeller av lasersvetsningsprocesser fungerar tillfredsställande. Det finns fortfarande ett arbete att göra för att få full mognad för denna modell på grund av dess begränsning och vissa antaganden för enkelhetens skull.

<p dir="ltr">Neural operator learning is emerging as a prominent technique in scientific machine learn- ing for modeling complex nonlinear systems with multi-physics and multi-scale applications. A common drawback of such operators is that they are data-hungry and the results are highly dependent on the quality and quantity of the training data provided to the models. Moreover, obtaining high-quality data in sufficient quantity can be computationally prohibitive. Faster surrogate models are required to overcome this drawback which can be learned from datasets of variable fidelity and also quantify the uncertainty. In this work, we propose a Multi-Level Stacked Deep Operator Network (MLSDON) which can learn from datasets of different fidelity and is not dependent on the input function. Through various experiments, we demonstrate that the MLSDON can approximate the high-fidelity solution operator with better accuracy compared to a Vanilla DeepONet when sufficient high-fidelity data is unavailable. We also extend MLSDON to build robust confidence intervals by making conformalized predictions. This technique guarantees trajectory coverage of the predictions irrespective of the input distribution. Various numerical experiments are conducted to demonstrate the applicability of MLSDON to multi-fidelity, multi-scale, and multi-physics problems.</p>

<p dir="ltr">Powder Sprays such as Direct Energy Deposition and Cold Spray are rapidly growing and promising manufacturing methods in the Additive Manufacturing field, as they allow easy and localized delivery of powder to be fused to a substrate and consecutive layers. The relatively small size of nozzles allows for these methods to be mounted on CNC machines and Robotic Arms for the creation of complex shapes. However, these manufacturing methods are inherently stochastic, and therefore differences in powder size, shape, trajectory, and velocity can drastically affect whether they will deposit on a substrate. This variation results in an inherent reduction of deposition efficiency, leading to waste and the need for powder collection or recycling systems. The design of the nozzles can drastically affect the variation of powder trajectory and velocity on a holistic level, and thus understanding the gas-powder flow of these nozzles in respect to the features of said nozzles is crucial. This paper proposes and examines how changes in the nozzle geometry affect gas-powder flow and powder focusing for Direct Energy Deposition and Cold Spray. In addition, a new Pulsed Cold Spray nozzle design is proposed that will control the amount of gas and powder used by the nozzle via solenoid actuation. By making these changes to the nozzle, it is possible to improve deposition efficiency and reduce powder/gas waste in these processes, while also allowing for improved coating density. Furthermore, the research done in this thesis will also focus on novel applications to powder spray manufacturing methods, focusing on polymer metallization and part identification.</p>

<p dir="ltr">For additive manufacturing to be a viable method to build metal parts for industries such as nuclear, the manufactured parts must be of higher quality and have lower variation in said quality than what can be achieved today. This high variation in quality bars the techniques from being used in high safety tolerance fields, such as nuclear. If this obstacle could be overcome, the benefits of additive manufacturing would be in lower cost for complex parts, as well as the ability to design and test parts in a very short timeframe, as only the CAD model needs to be created to manufacture the part. In this study, work to achieve this lower variation of quality was approached in two ways. The first was in the development of surrogate models, utilizing machine learning, to predict the end quality of additively manufactured parts. This was done by using experimental data for the mechanical properties of built parts as outputs to be predicted, and in-situ signals captured during the manufacturing process as inputs to the model. To capture the in-situ signals, cameras were used for thermal and optical imaging, leveraging the natural layer-by-layer manufacturing method used in AM techniques. The final models were created using support vector machine and gaussian process regression machine learning algorithms, giving high correlations between the insitu signals and mechanical properties of relative density, elongation to fracture, uniform elongation, and the work hardening exponent. The second approach to this study was in the development of a reduced order model for a computer simulation of an AM build. For project, a ROM was built inside the MOOSE framework, and was developed for an AM model designed by the MOOSE team, using proper orthogonal decomposition to project the problem onto a lower dimensional subspace, using POD to design the reduced basis subspace. The ROM was able to achieve a reduction to 1% the original dimensionality of the problem, while only allowing 2-5% relative error associated with the projection.</p>

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.

<p dir="ltr">Positive displacement machines are used in various industries spanning the power spectrum, from industrial robotics to heavy construction equipment to aviation. These machines should be highly efficient, compact, and reliable. It is very advantageous for designers to use virtual simulations to design and improve the performance of these units as they significantly reduce cost and downtime. The recent trends of electrification and the goal to increase power density force these units to work at higher pressures and higher rotational speeds while maintaining their efficiencies and reliability. This push means that the simulation models need to advance to account for various aspects during the operation of these machines. </p><p dir="ltr">These machines typically have several bodies in relative motion with each other. Quantifying these motions and solving for their effect on the fluid enclosed are vital as they influence the machine's performance. The push towards higher rotational speeds introduces unwanted cavitation and aeration in these units. To model these effects, keeping the design evaluation time low is key for a designer. The lumped parameter approach offers the benefit of computational speed, but a major drawback that comes along with it is that it typically assumes fluid inertia to be negligible. These effects cannot be ignored, as quantifying and making design considerations to negate these effects can be beneficial. Therefore, this thesis addresses these key challenges of cavitation dynamics, body dynamics, and accounting for fluid inertia effects using a lumped parameter formulation.</p><p dir="ltr">To account for dynamics features related to cavitation, this thesis proposes a novel approach combining the two types of cavitation, i.e., gaseous and vaporous, by considering that both vapor and undissolved gas co-occupy a spherical bubble. The size of the spherical bubble is solved using the Rayleigh-Plesset equation, and the transfer of gas through the bubble interface is solved using Henry's Law and diffusion of the dissolved gas in the liquid. These equations are coupled with a novel pressure derivative equation. To account for body dynamics, this thesis introduces a novel approach for solving the positions of the bodies of a hydraulic machine while introducing new methods to solve contact dynamics and the application of Elasto Hydrodynamic Lubrication (EHL) friction at those contact locations. This thesis also proposes strategies to account for fluid inertia effects in a lumped parameter-based approach, taking as a reference an External Gear Machine. This thesis proposes a method to study the effects of fluid inertia on the pressurization and depressurization of the tooth space volumes of these units. The approach is based on considering the fluid inertia in the pressurization grooves and inside the control volumes with a peculiar sub-division. Further, frequency-dependent friction is also modeled to provide realistic damping of the fluid inside these channels.</p><p dir="ltr">To show the validity of the proposed dynamic cavitation model, the instantaneous pressure of a closed fluid volume undergoing expansion/compression is compared with multiple experimental sources, showing an improvement in accuracy compared to existing models. This modeling is then further applied to a gerotor machine and validated with experiments. Integrating this modeling technique with current displacement chamber simulation can further improve the understanding of cavitation in hydraulic systems. Formulations for body dynamics are tested on a prototype Gerotor and Vane unit. For both gerotor and vane units, comparisons of simulation results to experimental results for various dynamic quantities, such as pressure ripple, volumetric, and hydromechanical efficiency for multiple operating conditions, have been done. Extensive validation is performed for the case of gerotors where shaft torque ripple and the motion of the outer gear is experimentally validated. The thesis also comments on the distribution of the different torque loss contributions. The model for fluid inertia effects has been validated by comparing the lumped parameter model with a full three-dimensional Navier Stokes solver. The quantities compared, such as tooth space volume pressures and outlet volumetric flow rate, show a good match between the two approaches for varying operating speeds. A comparison with the experiments supports the modeling approach as well. The thesis also discusses which operating conditions and geometries play a significant role that governs the necessity to model such fluid inertia effects in the first place.</p>