<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>

<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>

The design and optimization of entrained flow gasifiers is conducted more and more via computational fluid dynamics (CFD). A detailed resolution of single coal particles within such simulations is nowadays not possible due to computational limitations. Therefore the coal particle conversion is often represented by simple 0-D models. For an optimization of such 0-D models a precise understanding of the physical processes at the boundary layer and within the particle is necessary. In real gasifiers the particles experience Reynolds numbers up to 10000. However in the literature the conversion of coal particles is mainly regarded under quiescent conditions. Therefore an analysis of the conversion of single particles is needed. Thereto the computational fluid dynamics can be used. For the detailed analysis of single reacting particles under flow conditions a CFD model is presented. Practice-oriented parameters as well as features of the CFD model result from CFD simulations of a Siemens 200MWentrained flow gasifier. The CFD model is validated against an analytical model as well as two experimental data-sets taken from the literature. In all cases good agreement between the CFD and the analytics/experiments is shown. The numerical model is used to study single moving solid particles under combustion conditions. The analyzed parameters are namely the Reynolds number, the ambient temperature, the particle size, the operating pressure, the particle shape, the coal type and the composition of the gas. It is shown that for a wide range of the analyzed parameter range no complete flame exists around moving particles. This is in contrast to observations made by other authors for particles in quiescent atmospheres. For high operating pressures, low Reynolds numbers, large particle diameters and high ambient temperatures a flame exists in the wake of the particle. The impact of such a flame on the conversion of the particle is low. For high steam concentrations in the gas a flame appears, which interacts with the particle and influences its conversion. Furthermore the impact of the Stefan-flow on the boundary layer of the particle is studied. It is demonstrated that the Stefan-flow can reduce the drag coefficient and the Nusselt number for several orders of magnitude. On basis of the CFD results two new correlations are presented for the drag coefficient and the Nusselt number. The comparison between the correlations and the CFD shows a significant improvement of the new correlations in comparison to archived correlations. The CFD-model is further used to study moving single porous particles under gasifying conditions. Therefore a 2-D axis-symmetric system of non-touching tori as well as a complex 3-D geometry based on the an inverted settlement of monodisperse spheres is utilized. With these geometries the influence of the Reynolds number, the ambient temperature, the porosity, the intrinsic surface and the size of the radiating surface is analyzed. The studies show, that the influence of the flow on the particle conversion is moderate. In particular the impact of the flow on the intrinsic transport and conversion processes is mainly negligible. The size of the radiating surface has a similar impact on the conversion as the flow in the regarded parameter range. On basis of the CFD calculations two 0-D models for the combustion and gasification of moving particles are presented. These models can reproduce the results predicted by the CFD sufficiently for a wide parameter range.

<div>A growing global population and improved standard of living in developing countries have resulted in an unprecedented increase in energy demand over the past several decades. While renewable energy sources are increasing, a huge portion of energy is still converted into useful work using heat engines. The combustion process in diesel and petrol engines releases carbon dioxide and other greenhouse gases as an unwanted side-effect of the energy conversion process. By improving the efficiency of internal combustion engines, more chemical energy stored in petroleum resources can be realized as useful work and, therefore, reduce global emissions of greenhouse gases. This research focused on improving the thermal efficiency of opposed-piston engines, which, unlike traditional reciprocating engines, do not use a cylinder head. The cylinder head is a major source of heat loss in reciprocating engines. Therefore, the opposed-piston engine has the potential to improve overall engine efficiency relative to inline or V-configuration engines.</div><div><br></div>The objective of this research project was to further improve the design of opposed-piston engines by using computational fluid dynamics (CFD) modeling to optimize the engine geometry. The CFD method investigated the effect of intake port geometry and exhaust piston lead angle on the scavenging process and in-cylinder turbulence. After the CFD data was analyzed, scavenging efficiency was found insensitive to transfer port geometry and exhaust piston lead angle with a maximum change of 0.61%. Trapping efficiency was altered exclusively by exhaust piston lead angle and changed from 18% to 26% as the lead angle was increased. The in-cylinder turbulence parameters of the engine (normalized swirl circulation, normalized tumble circulation, and normalized TKE) experienced more complex relationships. All turbulence parameters were sensitive to changing transfer port geometry and exhaust piston lead angle. Some examples of trends seen during the analysis include: an increase in normalized swirl circulation from 0.01 to 4.45 due to changes in swirl angle, a change in normalized tumble circulation from -28.52 to 21.11 as swirl angle increased, and an increase in normalized tumble circulation from 14.20 to 33.68 as exhaust piston lead angle was increased. Based on the present work, an optimum configuration was identified for a swirl angle of 15°, a tilt angle of 10°, and an exhaust piston lead angle of 20°. Future work includes expanding the numerical model’s domain to support a complete cylinder-port configuration, adding combustion products to the diffusivity equation in the UDF, and running additional test cases to describe the entire input space for the sensitivity analysis.<br>

The design and optimization of entrained flow gasifiers is conducted more and more via computational fluid dynamics (CFD). A detailed resolution of single coal particles within such simulations is nowadays not possible due to computational limitations. Therefore the coal particle conversion is often represented by simple 0-D models. For an optimization of such 0-D models a precise understanding of the physical processes at the boundary layer and within the particle is necessary. In real gasifiers the particles experience Reynolds numbers up to 10000. However in the literature the conversion of coal particles is mainly regarded under quiescent conditions. Therefore an analysis of the conversion of single particles is needed. Thereto the computational fluid dynamics can be used. For the detailed analysis of single reacting particles under flow conditions a CFD model is presented. Practice-oriented parameters as well as features of the CFD model result from CFD simulations of a Siemens 200MWentrained flow gasifier. The CFD model is validated against an analytical model as well as two experimental data-sets taken from the literature. In all cases good agreement between the CFD and the analytics/experiments is shown. The numerical model is used to study single moving solid particles under combustion conditions. The analyzed parameters are namely the Reynolds number, the ambient temperature, the particle size, the operating pressure, the particle shape, the coal type and the composition of the gas. It is shown that for a wide range of the analyzed parameter range no complete flame exists around moving particles. This is in contrast to observations made by other authors for particles in quiescent atmospheres. For high operating pressures, low Reynolds numbers, large particle diameters and high ambient temperatures a flame exists in the wake of the particle. The impact of such a flame on the conversion of the particle is low. For high steam concentrations in the gas a flame appears, which interacts with the particle and influences its conversion. Furthermore the impact of the Stefan-flow on the boundary layer of the particle is studied. It is demonstrated that the Stefan-flow can reduce the drag coefficient and the Nusselt number for several orders of magnitude. On basis of the CFD results two new correlations are presented for the drag coefficient and the Nusselt number. The comparison between the correlations and the CFD shows a significant improvement of the new correlations in comparison to archived correlations. The CFD-model is further used to study moving single porous particles under gasifying conditions. Therefore a 2-D axis-symmetric system of non-touching tori as well as a complex 3-D geometry based on the an inverted settlement of monodisperse spheres is utilized. With these geometries the influence of the Reynolds number, the ambient temperature, the porosity, the intrinsic surface and the size of the radiating surface is analyzed. The studies show, that the influence of the flow on the particle conversion is moderate. In particular the impact of the flow on the intrinsic transport and conversion processes is mainly negligible. The size of the radiating surface has a similar impact on the conversion as the flow in the regarded parameter range. On basis of the CFD calculations two 0-D models for the combustion and gasification of moving particles are presented. These models can reproduce the results predicted by the CFD sufficiently for a wide parameter range.:List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .XIII Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIX 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 State of the Art in Carbon Conversion Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Classification of the Present Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 1.3 Overview of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 2 Basic Theory and Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Geometry and Length Scales of Coal Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.2 Conditions in a Siemens Like 200 MW Entrained Flow Gasifier . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Velocity Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2.2 Temperature Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Particle Volume Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 2.3 Time Scales of the Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21 2.5 Conservation Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Gas Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 2.7 Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.8 Numerics and Solution Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 2.9 Mesh and Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31 3 CFD-based Oxidation Modeling of a Non-Porous Carbon Particle . . . . . . . . . . . . . . . . . . . . .37 3.1 Chemical Reaction System for Combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.1 Heterogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.2 Homogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 3.1.3 Comparison of the Semi-Global vs. Reduced Reaction Mechanisms for the Gas Phase . .41 3.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 3.2.1 Validation Against an Analytical Solution of the Two-Film Model . . . . . . . . . . . . . . . . . .43 3.2.2 Validation Against Experiments I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.3 Validation Against Experiments II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49 3.3 Influence of Ambient Temperature and Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . .51 3.4 Influence of Heterogeneous Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Influence of Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61 3.6 Influence of Operating Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66 3.7 Influence of Particle Diameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70 3.8 The influence of Particle Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.9 Impact of Stefan Flow on the Boundary Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.9.1 Impact of Stefan Flow on the Drag Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83 3.9.2 Impact of Stefan Flow on the Nusselt and Sherwood Number . . . . . . . . . . . . . . . . . . . .85 3.10 Single-Film Sub-Model vs. CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4 CFD-based Numerical Modeling of Partial Oxidation of a Porous Carbon Particle . . . . . . . . . .99 4.1 Chemical Reaction System for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.1.1 Heterogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100 4.1.2 Homogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Two-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.2 Influence of Reynolds Number and Ambient Temperature . . . . . . . . . . . . . . . . . . . . . .109 4.2.3 Influence of Porosity and Internal Surface . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 120 4.3 Comparative Three-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 4.3.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126 4.3.2 Results of the 3-D Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.4 Extended Sub-Model for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .141 5.1 Summary of This Work . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .141 5.2 Recommendations for Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145 6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.1 Appendix I: Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2 Appendix II: Two-Film Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 6.3 Appendix III: Sub-Model for the Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . 160 6.4 Appendix IV: Sub-Model for the Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . 161