❚✐t❧❡✿ ◗✉❛♥t✉♠ t✉r❜✉❧❡♥❝❡ ✐♥ s✉♣❡r✢✉✐❞ ❤❡❧✐✉♠ st✉❞✐❡❞ ❜② ♣❛rt✐❝❧❡ tr❛❝❦✐♥❣ ✈❡❧♦❝✐♠❡tr② ✈✐s✉❛❧✐③❛t✐♦♥ t❡❝❤♥✐q✉❡ ❆✉t❤♦r✿ ❘◆❉r✳ ❉❛♥✐❡❧ ❉✉❞❛ ❉❡♣❛rt♠❡♥t✿ ❉❡♣❛rt♠❡♥t ♦❢ ▲♦✇ ❚❡♠♣❡r❛t✉r❡ P❤②s✐❝s ❙✉♣❡r✈✐s♦r✿ ♣r♦❢✳ ❘◆❉r✳ ▲❛❞✐s❧❛✈ ❙❦r❜❡❦✱ ❉r❙❝✳ ❆❜str❛❝t✿ ❚❤❡ P❛rt✐❝❧❡ ❚r❛❝❦✐♥❣ ❱❡❧♦❝✐♠❡tr② ✈✐s✉❛❧✐③❛t✐♦♥ t❡❝❤♥✐q✉❡ ✉s✐♥❣ ♠✐❝✲ r♦♠❡t❡r s✐③❡ s♦❧✐❞ ❞❡✉t❡r✐✉♠ ♣❛rt✐❝❧❡s ❛s tr❛❝❡rs ❤❛s ❜❡❡♥ ❛♣♣❧✐❡❞ t♦ st✉❞② ♦s❝✐❧✲ ❧❛t♦r② ✢♦✇s ♦❢ ❍❡ ■■✱ ✇❤✐❝❤ ✐s ❛ q✉❛♥t✉♠ ✢✉✐❞ ✇✐t❤ q✉❛♥t✐③❡❞ ✈♦rt✐❝✐t②✱ ❛s ✇❡❧❧ ❛s ✢♦✇s ♦❢ ❍❡ ■✱ ✇❤✐❝❤ ✐s ❛ ❝❧❛ss✐❝❛❧ ✈✐s❝♦✉s ❧✐q✉✐❞✱ ❢♦❝✉s✐♥❣ ♦♥ t❤❡ s✐♠✐❧❛r✐t✐❡s ❛♥❞ ❞✐✛❡r❡♥❝❡s ❜❡t✇❡❡♥ t❤❡ q✉❛♥t✉♠ ❛♥❞ ❝❧❛ss✐❝❛❧ ✢♦✇s✳ ❚❤r❡❡ ❡①♣❡r✐♠❡♥ts ❛r❡ ❞❡s❝r✐❜❡❞✿ t❤❡ ✢♦✇ ♣❛st ❛ ❧❛r❣❡✲❛♠♣❧✐t✉❞❡ ❧♦✇✲❢r❡q✉❡♥❝② ♦s❝✐❧❧❛t✐♥❣ ♦❜st❛❝❧❡ ✐♥ t❤❡ ❢♦r♠ ♦❢ ❛ ♣r✐s♠❀ t❤❡ st❡❛❞② str❡❛♠✐♥❣ ✢♦✇ ❞✉❡ t♦ ❛ s♠❛❧❧✲❛♠♣❧✐t✉❞❡ ❧❛r❣❡✲ ❢r❡q✉❡♥❝② ♦s❝✐❧❧❛t✐♥❣ q✉❛rt③ t✉♥✐♥❣ ❢♦r❦ ✲ ❛ ✇✐❞❡❧② ✉s❡❞ t♦♦❧ t♦ st✉❞② q✉❛♥t✉♠ t✉r❜✉❧❡♥❝❡❀ ❛♥❞ t❤❡ ♣r♦❞✉❝t✐♦♥ ♦❢ ❝❛✈✐t❛t✐♦♥ ✐♥ t❤❡ ✈✐❝✐♥✐t② ♦❢ ❛ ❢❛st✲♦s❝✐❧❧❛t✐♥❣ t✉♥✐♥❣ ❢♦r❦✳ ❚❤❡ ♠❛✐♥ ♦✉t❝♦♠❡ ✐s t❤❡ ♦❜s❡r✈❛t✐♦♥ t❤❛t t❤❡s❡ ✢♦✇s ❛r❡ s✐♠✐❧❛r ✐♥ ❍❡ ■ ❛♥❞ ✐♥ ❍❡ ■■ ❛t ❧❛r❣❡ ❧❡♥❣t❤✲s❝❛❧❡s✱ ✇❤❡r❡❛s ❛t s♠❛❧❧ s❝❛❧❡s✱ t❤❡② ❡①❤✐❜✐t t♦t❛❧❧② ❞✐✛❡r❡♥t st❛t✐st✐❝❛❧ ♣r♦♣❡rt✐❡s✳ ▼♦r❡♦✈❡r✱ ✐♥ ❍❡ ■■✱ t❤❡s❡ s♠❛❧❧ s❝❛❧❡ st❛✲ t✐st✐❝❛❧ ♣r♦♣❡rt✐❡s ❛r❡ ✉♥✐✈❡rs❛❧ ✐♥ t❤❛t t❤❡② ❞♦ ♥♦t ❞❡♣❡♥❞ ♦♥ t❤❡ t②♣❡ ♦❢ t❤❡ ✐♠♣♦s❡❞ ♠❡❛♥ ✢♦✇ ♦❢ t❤❡ s✉♣❡r✢✉✐❞ ❛♥❞ ♥♦r♠❛❧...

The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. The tool should be able to time efficiently calculate flow parameters from the data, and automatically create overview images of the flow field while doing so, to aid the user's analysis process. The tool is coded using Python programming language, and the Python scripts are inputted to the application ParaView for processing of the simulation data.  The tool generates 3 chamber views of the heart by calculating three points from the given patient data, which represent the aortic and mitral valves, and the apex of the heart. A plane is generated that pass through these three points, and the heart is sliced along this plane to visualize 3 chambers of the heart. The camera position is also manipulated to optimize the 3 chamber view. The maximum outflow velocity over the cardiac cycle in the left atrial appendage (LAA) is determined by searching in a time range around the maximum outflow rate of the LAA in a cardiac cycle, and finding the highest velocity value that points away from the LAA in this range. The flow component analysis is calculated in the LAA and left ventricle (LV) by seeding particles in each at the start of the cardiac cycle, and tracking these particles forwards and backwards in time to determine where the particles end up and come from, respectively. By knowing these two aspects, the four different flow components of the blood can be determined in both the LAA and LV.  The tool can successfully create 3 chamber views of the heart model from three semi-automatically determined points, at a manipulated camera location. It can also calculate the maximum outflow velocity of the flow field over a cardiac cycle in the LAA, and perform a flow component analysis of the LAA and the LV by tracking particles forwards and backwards in time through a cardiac cycle. The maximum velocity calculation is relatively time efficient and produces results similar to those found manually, yet the output is dependent on the user-defined inputs and processing techniques, and varies between users. The flow component analysis is also time efficient, produces results for the LV that are comparable to pre-existing research, and produces results for the LAA that are comparable to the LVs' results. Although, the extraction process of the LAA sometimes includes part of the left atrium, which impacts the accuracy of the results. After processing each part, the tool creates a single file containing each part's main results for easier analysis of the patient data. In conclusion, the tool is capable of semi-automatically processing CFD simulation data which saves the user time, and it has thus met all the project aims

<p>Two-phase modeling and simulation capabilities are strongly dependent on the accuracy of flow regime identification methods. Flow regimes have traditionally been determined through visual observation, resulting in subjective classifications that are susceptible to inconsistencies and disagreements between researchers. Since the majority of two-phase flow studies have been concentrated around vertical and horizontal pipe orientations, flow patterns in inclined pipes are not well-understood. Moreover, they may not be adequately described by conventional flow regimes which were conceptualized for vertical and horizontal flows. Recent work has explored applying machine learning methods to vertical and horizontal flow regime identification to help remedy the subjectivity of classification. Such methods have not, however, been successfully applied to inclined flow orientations. In this study, two novel unsupervised machine learning methods are proposed: a modular configuration of multiple machine learning algorithms that is adaptable to different pipe orientations, and a second universal approach consisting of several layered algorithms which is capable of performing flow regime classification for data spanning multiple orientations. To support this endeavor, an experimental database is established using a dual-ring impedance meter. The signals obtained by the impedance meter are capable of conveying distinct features of the various flow patterns observed in vertical, horizontal, and inclined pipes. Inputs to the unsupervised learning algorithms consist of statistical measures computed from these signals. A novel conceptualization for flow pattern classification is developed, which maps three statistical parameters from the data to red, green, and blue primary color intensities. By combining the three components, a flow pattern map can be developed wherein similar colors are produced by flow conditions with like statistics, transforming the way flow regimes are represented on a flow regime map. The resulting dynamic RGB flow pattern map provides a physical representation of gradual changes in flow patterns as they transition from one regime to another. By replacing the static transition boundaries with physically informed, dynamic gradients between flow patterns, transitional flow patterns may be described with far greater accuracy. This study demonstrates the effectiveness of the proposed method in generating objective flow regime maps, providing a basis for further research on the characterization of two-phase flow patterns in inclined pipes. The three proposed methods are compared and evaluated against flow regime maps found in literature.</p>

<p>  </p> <p>The study of two-phase flow in different orientations can allow for greater understanding of the fundamentals of two-phase flow dynamics. While a large amount of work has been performed for vertical flows and recent work has been done for horizontal flows, limited research has been done studying inclined upward two-phase flows between those two orientations. Studying two-phase flows at various inclinations is important for developing physical models and simulations of two-phase flow systems and understanding the changes between what is observed for symmetric vertical flows and asymmetric horizontal flows. The present work seeks to systematically characterize the effects of inclination on adiabatic concurrent air-water two-phase flows in straight pipes. An experimental database is established for local and global two-phase flow parameters in a novel inclinable 25.4 mm inner diameter test facility using four-sensor conductivity probes, high speed video capabilities, a ring-type impedance meter, a pressure transducer, and a gamma densitometer. Rotatable measurement ports are employed to allow for local conductivity probe measurements across the flow profile to capture asymmetric parameter distributions during experiments without stopping the flow. Some of the major effects of inclination are investigated, including the effects on flow regime transition, bubble distribution, frictional pressure loss, and relative motion between the two phases. Flow visualization and machine-learning methods are employed to identify the transitions between flow regimes for inclined orientations, and these transitions are compared against existing theoretical flow regime transition criteria proposed in literature. The theoretical transitions in literature agree well with both methods for vertical flow, but additional work is necessary for angles between 0 degrees and 60 degrees. The effect of inclination on two-phase frictional pressure drop is explored, and a novel adaption of the Lockhart-Martinelli pressure drop correlation is proposed, which is able to predict the pressure drop for the conditions investigated with an absolute percent difference of 2.6%. To explore the relationships between orientation, void fraction, and relative motion, one-dimensional drift flux analyses are performed for the data at each angle investigated. It is observed that the relative velocity between phases decreases as the angle is reduced, with a relative velocity near zero at some intermediate angles and a negative relative velocity for near-horizontal orientations.  Existing modeling capabilities that have been developed for vertical and horizontal flows are evaluated based on the local two-phase parameters collected at multiple orientations. The performance of the one-dimensional interfacial area transport equation for vertical and horizontal flows is tested against experimental data and a novel model for horizontal and inclined-upward bubbly flows is proposed. Finally, an evaluation of existing momentum transfer relations is performed for the two-fluid model using three-dimensional computational fluid dynamics tools for horizontal and inclined. The prediction of the void fraction distribution and gas velocity profiles are compared against experimental data, and improvements to the lift force model are identified based on changes in the relative velocity between phases. </p>

<p></p><p>This doctoral dissertation takes the reader through a journey where applied shear rheology and flow-velocimetry are used to understand the mesoscopic factors that control the flow behavior of three microstructured fluids. Three individual protocols that measure relative physical and mechanical properties of the flow are developed. Each protocol aims to advance the particular transformation of novel soft materials into a commercial product converging in the demonstration of the real the chemical, physical and thermodynamical factors that could potentially drive their successful transformation. </p> <p> </p> <p>First, this dissertation introduces the use of rotational and oscillatory shear rheometry to quantify the solvent evaporation effect on the flow behavior of polymer solutions used to fabricate isoporous asymmetric membranes. Three different A-B-C triblock copolymer were evaluated: polyisoprene-<i>b</i>-polystyrene-<i>b</i>-poly(4-vinylpyridine) (ISV); polyisoprene-<i>b</i>-polystyrene-<i>b</i>-poly(<i>N</i>,<i>N</i>-dimethylacrylamide) (ISD); and polyisoprene-<i>b</i>-polystyrene-<i>b</i>-poly(<i>tert</i>-butyl methacrylate) (ISB). The resulting evaporation-induced microstructure showed a solution viscosity and film viscoelasticity strongly dependent on the chemical structure of the triblock copolymer molecules. </p> <p> </p> <p>Furthermore, basic shear rheometry, flow birefringence, and advanced flow-velocimetry are used to deconvolute the flow-microstructure relationships of concentrated surfactant solutions. Sodium laureth sulfate in water (SLE₁S) was used to replicate spherical, worm-like, and hexagonally packed micelles and lamellar structures. Interesting findings demonstrated that regular features of flow curves, such as power-law shear thinning behavior, resulted from a wide variety of experimental artifacts that appeared when measuring microstructured fluids with shear rheometry.</p> <p> </p> <p>Finally, the successful integration of shear rheometry to calculate essential parameters to be used in a cost-effective visualization technique (still in development) used to calculate the dissolution time of polymers is addressed. The use of oscillatory rheometry successfully quantify the viscoelastic response of polyvinyl alcohol (PVA) solutions and identify formulations changes such as additive addition. The flow behavior of PVA solutions was correlated to dissolution behavior proving that the developed protocol has a high potential as a first screening tool.</p><br><p></p>