A significant amount of Viscous Oil (e.g., heavy oil, extra heavy oil, and bitumen) is trapped in Naturally Fractured Carbonate Reservoirs also known as NFCRs. The word VO endowment in NFCRs is estimated at ~ 2 Trillion barrels mostly reported in Canada, the USA, Russia, and the Middle East. To date, contributions to the world daily oil production from this immense energy resource remains negligible mainly due to the lack of appropriate production technologies. Implementation of a VO production technology such as steam injection is expensive (high capital investment), time-consuming, and people-intensive. Hence, before selecting a production technology for detailed economic analysis, use of cursory or broad screening tools or guides is a convenient means of gaining a quick overview of the technical feasibility of the various possible production technologies applied to a particular reservoir. Technical screening tools are only available for the purpose of evaluation of the reservoir performance parameters in oil sands for various thermal VO exploitation technologies such as Steam Assisted Gravity Drainage (SAGD), Cyclic Steam Stimulation (CSS), Horizontal well Cyclic steam Stimulation (HCS), and so on. Nevertheless, such tools are not applicable for VO NFCRs assessment without considerable modifications due to the different nature of these two reservoir types (e.g., presence and effects of fracture network on reservoir behavior, wettability, lithology, fabric, pore structure, and so on) and also different mechanisms of energy and mass transport. Considering the lack of robust and rapid technical reservoir screening tools for the purpose of quick assessment and performance prediction for VO NFCRs under thermal stimulation (e.g., steamflooding), developing such fast and precise tools seems inevitable and desirable. In this dissertation, an attempt was made to develop new screening tools for the purpose of reservoir performance prediction in VO NFCRs using all the field and laboratory available data on a particular thermal technology (vertical well steamflooding). Considering the complex and heterogeneous nature of the NFCRs, there is great uncertainty associated with the geological nature of the NFCRs such as fracture and porosity distribution in the reservoir which will affect any modeling tasks aiming at modeling of processes involved in thermal VO production from these types of technically difficult and economically unattractive reservoirs. Therefore, several modeling and analyses technqiues were used in order to understand the main parameters controlling the steamflooding process in NFCRs and also cope with the uncertainties associated with the nature of geologic, reservoir and fluid properties data. Thermal geomechanics effects are well-known in VO production from oil sands using thermal technologies such as SAGD and cyclic steam processes. Hence, possible impacts of thermal processes on VO NFCRs performance was studied despite the lack of adequate field data. This dissertation makes the following contributions to the literature and the oil industry: Two new statistical correlations were developed, introduced, and examined which can be utilized for the purpose of estimation of Cumulative Steam to Oil Ratio (CSOR) and Recovery Factor (RF) as measures of process performance and technical viability during vertical well steamflooding in VO Naturally Fractured Carbonate Reservoirs (NFCRs). The proposed correlations include vital parameters such as in situ fluid and reservoir properties. The data used are taken from experimental studies and also field trials of vertical well steamflooding pilots in viscous oil NFCRs reported in the literature. The error percentage for the proposed correlations is < 10% for the worst case and contains fewer empirical constants compared with existing correlations for oil sands. The interactions between the parameters were also considered. The initial oil saturation and oil viscosity are the most important predictive factors. The proposed correlations successfully predicted steam/oil ratios and recovery factors in two heavy oil NFCRs. These correlations are reported for the first time in the literature for this type of VO reservoirs. A 3-D mathematical model was developed, presented, and examined in this research work, investigating various parameters and mechanisms affecting VO recovery from NFCRs using vertical well steamflooding. The governing equations are written for the matrix and fractured medium, separately. Uncertainties associated with the shape factor for the communication between the matrix and fracture is eliminated through setting a continuity boundary condition at the interface. Using this boundary condition, the solution method employed differs from the most of the modeling simulations reported in the literature. A Newton-Raphson approach was also used for solving mass and energy balance equations. RF and CSOR were obtained as a function of steam injection rate and temperature and characteristics of the fractured media such as matrix size and permeability. The numerical solution clearly shows that fractures play an important role in better conduction of heat into the matrix part. It was also concluded that the matrix block size and total permeability are the most important parameters affecting the dependent variables involved in steamflooding. A hybrid Artificial Neural Network model optimized by co-implementation of a Particle Swarm Optimization method (ANN-PSO) was developed, presented, and tested in this research work for the purpose of estimation of the CSOR and RF during vertical well steamflooding in VO NFCRs. The developed PSO-ANN model, conventional ANN models, and statistical correlations were examined using field data. Comparison of the predictions and field data implies superiority of the proposed PSO-ANN model with an absolute average error percentage < 6.5% , a determination coefficient (R2) > 0.98, and Mean Squared Error (MSE) < 0.06, a substantial improvement in comparison with conventional ANN model and empirical correlations for prediction of RF and CSOR. This indicates excellent potential for application of hybrid PSO-ANN models to screen VO NFCRs for steamflooding. This is the first time that the ANN technique has been applied for the purpose of performance prediction of steamflooding in VO NFCRs and also reported in the literature. The predictive PSO-ANN model and statistical correlations have strong potentials to be merged with heavy oil recovery modeling softwares available for thermal methods. This combination is expected to speed up their performance, reduce their uncertainty, and enhance their prediction and modeling capabilities. An integrated geological-geophysical-geomechanical approach was designed, presented, and applied in the case of a NFCR for the purpose of fracture and in situ stresses characterization in NFCRs. The proposed methodology can be applied for fracture and in situ stresses characterization which is beneficial to various aspects of asset development such as well placement, drilling, production, thermal reservoir modeling incorporating geomechanics effects, technology assessment and so on. A conceptual study was also conducted on geomechanics effects in VO NFCRs during steamflooding which is not yet well understood and still requires further field, laboratory, and theoretical studies. This can be considered as a small step forward in this area identifying positive potential of such knowledge to the design of large scale thermal operations in VO NFCRs.

Le transfert radiatif dû aux gaz joue un rôle important dans les applications industrielles comme les chambres de combustion, les sciences atmosphériques, etc. Plusieurs modèles ont été proposées pour estimer les propriétés radiatives des gaz. Le plus précis est l'approche dite Raie Par Raie (RPR). Cependant, cette méthode implique un coût de calcul excessif qui la rend inappropriée pour la plupart des applications. Néanmoins, elle reste la méthode de référence que nous utiliserons pour l'évaluation d’autres modèles approchés. Le modèle de coefficient d’absorption corrélé (Ck) est généralement suffisant pour de nombreuses applications. Cette méthode est réputée précise lorsque petits gradients de température sont rencontrés au sein du gaz. Toutefois, si le milieu gazeux est soumis à d'importants gradients de température, la méthode Ck peut conduire à des erreurs qui peuvent atteindre 50% en termes de flux radiatifs par rapport à des simulations de RPR. Le but de cette thèse est de proposer une version améliorée de la méthode Ck, appelée l'approche de coefficient d’absorption corrélé en multi spectral (MSCk). La principale différence entre les modèles Ck et MSCk est que, dans l'approche Ck les intervalles spectraux sur lesquels les propriétés radiatives des gaz sont moyennées sont choisis contiguës alors que, dans l’approche MSCk, ces intervalles sont construits afin d'assurer que le coefficient d'absorption soit corrélé sur ces intervalles. Par conséquent, l'hypothèse de corrélation dans l’approche MSCk est mieux adaptée que dans l’approche Ck. La construction de ces intervalles spectraux (en utilisant la méthode de classification automatique de données fonctionnelles) est détaillée. Cette approche est évaluée par rapport à la référence RPR dans plusieurs cas test. Ces cas traitent de mélanges de gaz (H2O-N2 et H2O-CO2-N2) dans l’intervalle de température [300-3000K]. Les résultats montrent que la méthode MSCk permet d'obtenir de meilleures précisions que les méthodes Ck tout en restant acceptable en termes de coût de calcul. / Radiative heat transfer of gas plays an important role in industrial applications such as in combustion chambers, atmospheric sciences, etc. Several models [11] have been proposed to estimate the radiative properties of gases. The most accurate one is the Line-By-Line (LBL) approach. However, this technique involves excessive computation cost which makes it inappropriate for most applications. Nevertheless, it remains the reference approach for the assessment of other approximate models. The Correlated k-distribution method (Ck) [11] was shown to be a relevant choice for many applications. This method performs usually well, when only small temperature gradients are involved [21]. However, if the gaseous medium is subject to large temperature gradients, it may lead to errors that can reach 50% in terms of radiative heat fluxes when compared to LBL simulations [21]. The aim of the present paper is to propose an enhanced version of the Ck method, called the Multi-Spectral Correlated k-distribution approach (MSCk). The main difference between Ck and MSCk models is that in the Ck approach spectral intervals over which the radiative properties of the gas are averaged are chosen contiguous whereas, in the MSCk technique, those intervals are built in order to ensure that the absorption coefficient are scaled over them [27]. Accordingly, the usual assumption of correlated spectrum used in k-distribution approaches for the treatment of non uniformities is more acceptable in the MSCk case than in the Ck one. The building of those spectral intervals (using Functional Data Clustering, [52]) is detailed and the approach is assessed against LBL reference data in several test cases. These cases involve H2O-N2 and H2O-CO2-N2 mixtures in the [300-3000K] temperature range. Results show that the MSCk method enables to achieve better accuracies than Ck methods while remaining acceptable in terms of computational cost.

La pile à combustible est un générateur électrique en voie d'atteindre une maturité technologique et commerciale. Pour que ce moyen de production d'énergie puisse concurrencer des systèmes similaires, tels que les batteries et les groupes électrogènes, des obstacles restent néanmoins à franchir. L'un d'entre eux est la capacité de la pile à démarrer et fonctionner à température ambiante négative. Afin d'étudier le comportement à froid d'un système de type PEMFC, nous proposons la définition de plusieurs critères de performances exergétiques adaptés au fonctionnement de chaque module du système. Les modules sont ensuite caractérisés à température ambiante négative à l'aide de bancs d'essais dédiés. A partir des résultats expérimentaux obtenus, différents modèles empiriques ou semi-analytiques sont alors présentés pour la batterie, le compresseur et l'humidificateur. D'autre part, un modèle analytique thermique à l'échelle des stacks est réalisé. Il permet notamment de reproduire l'élévation en température de la pile au cours d'un démarrage à froid. Enfin, à l'issue de l'analyse des résultats expérimentaux et des modèles, des recommandations destinées à favoriser le démarrage à froid du système sont fournies. En suivant ces recommandations, il est ainsi possible de démarrer le système pile de manière fiable à une température ambiante de -10 °C. / Fuel cells are electric generators on the way to achieve technological and commercial maturity. Nevertheless, to compete with similar energy generating systems such as batteries and engines generators, fuel cells must overcome several obstacles. Among them, the ability to start at negative ambient temperatures is decisive. In order to study the behaviour of a PEMFC system in cold weather, we propose different exergetic criteria adapted to the working conditions of each module. Thanks to dedicated test benches, the modules are then characterized at negative ambient temperature. From experimental results, empirical or semi-analytical models are introduced for the battery, the compressor and the humidifier. On the other hand, a thermal analytical model at the stacks scale is developed. It enables to reproduce the fuel cell temperature rise during a cold start up. Eventually, at the end of the analysis of experimental results and models, recommendations are given to favour the cold start of the system. By following these recommendations, the fuel cell cold start at -10 °C is ensured.

<p>  </p> <p>With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work.</p> <p>To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. </p> <p>This study incorporates a new computational platform, called <em>InVascular</em>, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI.</p> <p>As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.</p>

<p>Two studies incorporated providing the groundwork for a blueprint to design sodium sulfur cells from electrode fabrication to choices in electrolyte such as DME, DEGDME, TEGDME and two different salts NaClO4 and NaPF6. First study describes role of the binder within the system comparing carboxymethyl cellulose and carboxymethyl cellulose with a styrene butadiene elastomer addition. The second study focuses on methods to prevent polysulfide shuttling within room temperature sodium sulfur system</p>

<p>Subcutaneous administration has become a common approach for drug delivery of biotherapeutics, such as monoclonal antibodies, which is achieved mainly by absorption through the lymphatic system. This dissertation focuses on the computational modeling of the fluid flow and solute transport in the skin tissue and the quantitative investigation of lymphatic uptake. First, the various mechanisms governing drug transport and lymphatic uptake of biotherapeutics through subcutaneous injection are investigated quantitatively through high-fidelity numerical simulations, including lymphatic drainage, blood perfusion, binding, and metabolism. The tissue is modeled as a homogeneous porous medium using both a single-layered domain and a multi-layered domain, which includes the epidermis, dermis, hypodermis (subcutaneous tissue), and muscle layers. A systematic parameter study is conducted to understand the roles of different properties of the tissue in terms of permeability, porosity, and vascular permeability. The role of binding and metabolism on drug absorption is studied by varying the binding parameters for different macromolecules after coupling the transport equation with a pharmacokinetic equation. The interstitial pressure plays an essential role in regulating the absorption of unbound drug proteins during the injection, while the binding and metabolism of drug molecules reduce the total free drugs. </p> <p>  </p> <p>The lymphatic vessel network is essential to achieve the functions of the lymphatic system. Thus, the drug transport and lymphatic uptake through a three-dimensional hybrid discrete-continuum vessel network in the skin tissue are investigated through high-fidelity numerical simulations. The explicit heterogeneous vessel network is embedded into the continuum model to investigate the role of explicit heterogeneous vessel network in drug transport and absorption. The solute transport across the vessel wall is investigated under various transport conditions. The diffusion of the drug solutes through the explicit vessel wall affects the drug absorption after the injection, while the convection under large interstitial pressure dominates the drug absorption during the injection. The effect of diffusion cannot be captured by the previously developed continuum model. Furthermore, the effects of injection volume and depth on the lymphatic uptake are investigated in a multi-layered domain. The injection volume significantly affects lymphatic uptake through the heterogeneous vessel network, while the injection depth has little influence. At last, the binding and metabolism of drug molecules are studied to bridge the simulation to the experimentally measured drug clearance. </p> <p><br></p> <p>Convective transport of drug solutes in biological tissues is regulated by the interstitial fluid pressure, which plays a crucial role in drug absorption into the lymphatic system through the subcutaneous (SC) injection.  An approximate continuum poroelasticity model is developed to simulate the pressure evolution in the soft porous tissue during an SC injection. This poroelastic model mimics the deformation of the tissue by introducing the time variation of the interstitial fluid pressure. The advantage of this method lies in its computational time efficiency and simplicity, and it can accurately model the relaxation of pressure. The interstitial fluid pressure obtained using the proposed model is validated against both the analytical and the numerical solution of the poroelastic tissue model. The decreasing elasticity elongates the relaxation time of pressure, and the sensitivity of pressure relaxation to elasticity decreases with the hydraulic permeability, while the increasing porosity and permeability due to deformation alleviate the high pressure. </p> <p><br></p> <p>At last, an improved Kedem-Katchalsky model is developed to study solute transport across the lymphatic vessel network, including convection and diffusion in the multi-layered poroelastic tissue with a hybrid discrete-continuum vessel network embedded inside. The effect of different drug solutes with different Stokes radii and different structures of the lymphatic vessel network, such as fractal trees and Voronoi structure, on the lymphatic uptake is investigated. The drug solute with a small size has a larger partition coefficient and diffusivity across the openings of the lymphatic vessel wall, which favors drug absorption. The Voronoi structure is found to be more efficient in lymphatic uptake. </p> <p><br></p> <p>The systematic and quantitative investigation of subcutaneous absorption based on high-fidelity numerical simulations can provide guidance on the optimization of drug delivery systems and is valuable for the translation of bioavailability from the pre-clinical species to humans. We provide a novel approach to studying the diffusion and convection of drug molecules into the lymphatic system by developing the hybrid discrete-continuum vessel network. The study of the solute transport across the discrete lymphatic vessel walls further improves our understanding of lymphatic uptake. The novel and time-efficient computational model for solute transport across the lymphatic vasculature connects the microscopic properties of the lymphatic vessel membrane to macroscopic drug absorption. The comprehensive hybrid vessel network model developed here can be further used to improve our understanding of the diseases caused by the disturbed lymphatic system, such as lymphedema, and provide insights into the treatment of diseases caused by the malfunction of lymphatics.</p>

<p>Gaussian Process Regression (GPR) is a widely used surrogate model in efficient global optimization (EGO) due to its capability to provide uncertainty estimates in the prediction. The cost of creating a GPR model for large data sets is high. On the other hand, neural network (NN) models scale better compared to GPR as the number of samples increase. Unfortunately, the uncertainty estimates for NN prediction are not readily available. In this work, a scalable algorithm is developed for EGO using NN-based prediction and uncertainty (EGONN). Initially, two different NNs are created using two different data sets. The first NN models the output based on the input values in the first data set while the second NN models the prediction error of the first NN using the second data set. The next infill point is added to the first data set based on criteria like expected improvement or prediction uncertainty. EGONN is demonstrated on the optimization of the Forrester function and a constrained Branin function and is compared with EGO. The convergence criteria is based on the maximum number of infill points in both cases. The algorithm is able to reach the optimum point within the given budget. The EGONN is extended to handle constraints explicitly and is utilized for aerodynamic shape optimization of the RAE 2822 airfoil in transonic viscous flow at a free-stream Mach number of 0.734 and a Reynolds number of 6.5 million. The results obtained from EGONN are compared with the results from gradient-based optimization (GBO) using adjoints. The optimum shape obtained from EGONN is comparable to the shape obtained from GBO and is able to eliminate the shock. The drag coefficient is reduced from 200 drag counts to 114 and is close to 110 drag counts obtained from GBO. The EGONN is also extended to handle uncertainty quantification (uqEGONN) using prediction uncertainty as an infill method. The convergence criteria is based on the relative change of summary statistics such as mean and standard deviation of an uncertain quantity. The uqEGONN is tested on Ishigami function with an initial sample size of 100 samples and the algorithm terminates after 70 infill points. The statistics obtained from uqEGONN (using only 170 function evaluations) are close to the values obtained from directly evaluating the function one million times. uqEGONN is demonstrated on to quantifying the uncertainty in the airfoil performance due to geometric variations. The algorithm terminates within 100 computational fluid dynamics (CFD) analyses and the statistics obtained from the algorithm are close to the one obtained from 1000 direct CFD based evaluations.</p>

<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>Uncertainty analysis is of great use both for calculating outputs that are more akin to real<br> flight, and for optimization to more robust shapes. However, implementation of uncertainty<br> has been a longstanding challenge in the field of aerodynamics due to the computational cost<br> of simulations. Geometric uncertainty in particular is often left unexplored in favor of uncer-<br> tainties in freestream parameters, turbulence models, or computational error. Therefore, this<br> work proposes a method of geometric uncertainty analysis for aerodynamic shapes that miti-<br> gates the barriers to its feasible computation. The process takes a two- or three-dimensional<br> shape and utilizes a combination of multifidelity meshes and Gaussian process regression<br> (GPR) surrogates in a multifidelity Monte Carlo (MFMC) algorithm. Multifidelity meshes<br> allow for finer sampling with a given budget, making the surrogates more accurate. GPR<br> surrogates are made practical to use by parameterizing major factors in geometric uncer-<br> tainty with only four variables in 2-D and five in 3-D. In both cases, two parameters control<br> the heights of steps that occur on the top and bottom of airfoils where leading and trailing<br> edge devices are attached. Two more parameters control the height and length of waves<br> that can occur in an ideally smooth shape during manufacturing. A fifth parameter controls<br> the depth of span-wise skin buckling waves along a 3-D wing. Parameters are defined to<br> be uniformly distributed with a maximum size of 0.4 mm and 0.15 mm for steps and waves<br> to remain within common manufacturing tolerances. The analysis chain is demonstrated<br> with two test cases. The first, the RAE2822 airfoil, uses transonic freestream parameters<br> set by the ADODG Benchmark Case 2. The results show a mean drag of nearly 10 counts<br> above the deterministic case with fixed lift, and a 2 count increase for a fixed angle of attack<br> version of the case. Each case also has small variations in lift and angle of attack of about<br> 0.5 counts and 0.08◦, respectively. Variances for each of the three tracked outputs show that<br> more variability is possible, and even likely. The ONERA M6 transonic wing, popular due<br> to the extensive experimental data available for computational validation, is the second test<br> case. Variation is found to be less substantial here, with a mean drag increase of 0.5 counts,<br> and a mean lift increase of 0.1 counts. Furthermore, the MFMC algorithm enables accurate<br> results with only a few hours of wall time in addition to GPR training. </p>

<p>Free surface flows, characterized by a deformable interface between two immiscible fluids or between a liquid and a gas, play a pivotal role in numerous natural phenomena and industrial processes. The fluid-fluid interface dynamics, governed by the complex interplay of forces such as inertia, capillary force, viscous force, and possibly elastic force, significantly influence the behavior of the fluids involved. Examples of free surface flows can be observed in everyday situations, such as droplet formation from a faucet, propagation and breaking of ocean waves, and tear films that coat the eye. An in-depth understanding of free surface flows and fluid-fluid interface dynamics has extensive implications for optimizing applications like inkjet printing, coating, spraying, and droplet formation while providing insights into the intricate behavior of natural fluid systems. Most of these applications, except for coating, involve abrupt and catastrophic topological changes of interfaces present in processes such as drop breakup, sheet rupture, and drop impact, where small droplets form from liquid sheets or filaments.</p> <p>This thesis examines the dynamics of contracting liquid filaments through computational means. Previous computational simulations have assumed that initially the fluid within the filament is quiescent which, however, may not typically be the case in practical applications. Here, the effect of a realistic, non-zero initial velocity profile is considered with the hypothesis that the fact that the fluid is already in motion when it starts to contract may result in significant alterations in the filament’s final fate vis-a-vis whether it breaks up into multiple small droplets or contracts into a sphere as its ends retract toward each other. The transient system of governing equations, the three-dimensional but axisymmetric (3DA) Navier-Stokes and continuity equations subjected to interfacial boundary conditions, are solved using rigorous and robust numerical algorithms in both fully 3DA and one-dimensional (1D) settings using the Galerkin finite element (GFEM) method. The simulation results are then used to construct comprehensive phase diagrams to delineate regions where filaments break up into smaller droplets from those where filaments contract to spheres without breakup.</p> <p>Polymer additives are often present in practical applications involving filament contraction and breakup. The presence of polymer molecules in an otherwise Newtonian solvent gives rise to non-Newtonian rheology. In this thesis, the dynamics of filaments containing polymer additives are analyzed using a 1D algorithm that is developed specifically for simulating viscoelastic free surface flows where the fluid’s rheology is described by the oft-used Oldroyd-B model. In real-world applications, filaments produced from nozzles are expected to be prestressed at the instant when they are created and begin to contract. It is demonstrated that the retraction velocity of tips of highly viscous, prestressed filaments is significantly increased compared to filaments in which the polymer molecules are initially relaxed and Newtonian filaments. This enhancement is explained by examining the value of f σ: D (σ: Elastic stress; D: Rate-of-strain tensor), which can be positive or negative. This quantity is positive when the flow does work on the polymer molecules but negative when the molecules do work on the flow, i.e., when elastic recoiling or unloading takes place. In prestressed filaments, elastic unloading takes place because σ: D < 0. The elastic stresses work by pulling the fluid in axially and pushing it out radially, thereby drastically increasing the tip velocity.  However, this enhancement in contraction velocity is not observed in low to intermediate viscosity prestressed filaments and whose Newtonian counterparts typically experience end-pinching. It has been established by others that end-pinching can be precluded in either filaments of intermediate viscosity or surfactant-laden filaments of low viscosity through a process known as escape from end-pinching. In this study, we demonstrate that a similar escape can also occur in prestressed viscoelastic filaments of low-to-intermediate viscosity, as revealed by one-dimensional numerical simulations and rationalized by examining when and where the elastic recoil takes place.</p> <p>Beyond cylindrical filaments, thin liquid films or planar liquid sheets are also prevalent in atomization, curtain coating, and other processes where liquid sheet stability has been a subject of extensive research. Numerous authors have examined wave formation and growth leading to sheet breakup. Free liquid films or sheets without edges or caps at their two ends, which typically have two free surfaces and are surrounded by air or sometimes another liquid, can destabilize and rupture due to intermolecular van der Waals attractive forces, despite the stabilizing influence of surface tension. In this thesis, the dynamics of contracting free films or sheets with caps---two-dimensional (2D) drops---of Newtonian fluids is examined without considering van der Waals forces to confirm or refute the hypothesis that such systems can rupture due to finite-amplitude perturbations even in the absence of intermolecular forces. In particular, both two-dimensional and one-dimensional high-accuracy simulations are employed to demonstrate that unlike inviscid 2D drops that can rupture in the absence of van der Waals forces, 2D drops or sheets can escape from pinch-off due to the action of viscous forces which are present in real systems no matter how small their viscosity. The reopening of the interface and escape from pinch-off in 2D drops and sheets are explained by demonstrating the key role played by vorticity. New power-law relations or scaling laws are obtained as a function of Ohnesorge number (ratio of viscous to the square root of the product of inertial and capillary forces) for the value of the minimum film thickness for which 2D drops or sheets stop thinning and after which the interface begins to reopen. Simple yet powerful arguments are presented rationalizing these scaling laws. It is expected that these power-law relations should be of great interest to experimentalists who study such phenomena by high-speed visualization experiments.</p> <p>Some of the motivation for this thesis research comes from crop spraying applications in which achieving zero or negligible drift is highly desirable. To further the understanding of fluid mechanics underpinning current and future drift reduction technologies, a simplified experimental setup is adopted to generate liquid sheets and analyze their disintegration into droplets. This new setup is both simpler and more universal than commonly utilized experimental systems that use single or multiple nozzles to generate liquid sheets and spray droplets from the disintegration of free liquid films. In the current experiments, droplets of test fluids are made to collide with or impact the top planar surface of a solid cylinder or rod. A series of MATLAB codes are developed and employed to extract droplet size distributions from images that are obtained from high-speed visualization experiments. The experimental setup and the means of data analysis are then used to probe the effect of fluid properties on the dynamics of sheet disintegration and droplet size distributions. It is hoped that future researchers will be able to combine what has been done in this thesis by simulations and in this chapter via experimental observations to develop an improved mechanistic understanding of spray formation.</p>