<p dir="ltr">This dissertation describes efforts in the development of an optically-accessible solid fuel ramjet combustion experiment and the application, and requisite modifications, of multiple laser-based diagnostics. These measurements target the characterization of the complex turbulent reacting flow physics in a multi-phase combustion environment representative of conditions within a solid fuel ramjet.</p><p dir="ltr"><br>First, dynamic flow-flame interactions were investigated in an optically-accessible solid fuel ramjet combustor. Experiments were performed with a single hydroxyl-terminated polybutadiene fuel slab located downstream of a backward-facing step in a rectangular chamber. To emulate flight-relevant combustor conditions, unvitiated heated air was directed through the combustion chamber with an inlet temperature of ∼655 K, chamber pressures of 450–690 kPa, and port Reynolds number of ∼500,000. 20 kHz OH∗-chemiluminescence and 10 kHz particle imaging velocimetry measurements were used to characterize the heat-release distribution and velocity field. Comparison between the mean OH∗ chemiluminescence images acquired at three flow conditions indicates reduction in flame height above the grain with increasing air mass flow rate. Dominant heat-release coherent structures in the statistically stationary flow are identified using the spectral proper orthogonal decomposition technique implemented on time-series of instantaneous images. The spatial mode shapes of the chemiluminescence and velocity field measurements indicated that the flow-flame interactions were dominated by vortex shedding generated at the backward facing step in the combustor, at Strouhal numbers of 0.06 – 0.10.</p><p dir="ltr"><br>Following this effort, a coherent anti-Stokes Raman scattering (CARS) laser system was assembled and aligned for measurements of the Q-branch ro-vibrational energy level structure of nitrogen using a coannular phase-matching scheme and frequency-shifted probe beam. These measurements were demonstrated in the model SFRJ combustion chamber operated with an inlet air temperature of 690 K and pressure of 0.59 MPa. Over 300 single-shot spectra were collected and fit for temperatures ranging from the core air flow to the combustion gases with a probe location situated above the redeveloping boundary layer region diffusion flame. A skewed temperature distribution was reported at the probe location, as expected from a region only intermittently exposed to hot combustion gases. Temperatures of 500-2000 K were fit to theory, indicating a requirement for high dynamic range measurements.</p><p dir="ltr"><br>A handful of shortcomings were identified in the application of the shifted-CARS technique to the luminous SFRJ flow-field and thus modifications were made to the CARS system for improved dynamic range, signal-to-noise ratio and signal-to-interference ratio. A dual-pump system provided simultaneous measurements of the Q-branch ro-vibrational energy level structure of nitrogen and pure-rotational energy level structure of nitrogen and oxygen. These spectra possessed ample features for accurate comparison to theory at temperatures of 600-2500 K, a typical range at flame locations within the highly dynamic SFRJ reacting flow. Additionally, an electro-optical shutter (EOS), comprised of a Pockels cell located between crossed-axis polarizers, was integrated into the CARS system. The use of the EOS enabled thermometry measurements in high luminosity flames through significant reduction of the background resulting from broadband flame emission. Temporal gating ≤100 nanoseconds along with an extinction ratio >10,000:1 was achieved using the EOS. Integration of the EOS enabled the use of an unintensified CCD camera for signal detection, improving upon the signal-to-noise ratio achievable with inherently noisy microchannel plate intensification processes, previously employed for short temporal gating.<br></p><p dir="ltr">Using this system, temperature and relative oxygen concentration scalar fields were measured in an optically accessible solid fuel ramjet (SFRJ) combustion chamber using coherent anti-Stokes Raman scattering (CARS). Additionally, planar laser-induced fluorescence measurements of the hydroxyl radical (OH-PLIF) were performed to spatially characterize flame location and provide context to the temperature measurements. The combustion chamber was operated with an inlet air temperature of 670 K, mass flowrate of 1.14 kg/s, and pressure of 0.57 MPa, conditions relevant to practical device operation. The dual-pump CARS system provided simultaneous measurements of the Q-branch ro-vibrational energy level structure of nitrogen and pure-rotational energy level structure of nitrogen and oxygen. These spectra possessed ample features for accurate comparison to theory at temperatures of 600-2500 K, a typical range at flame locations within the highly dynamic SFRJ reacting flow<br>and inherently track the relative oxygen concentration within the measurement volume. A skewed temperature distribution was reported at various probe locations, as expected from stochastic probing of dynamic reacting vortex structures. Comparison between CARS and OH-PLIF measurements within the flow impingement region indicated that the high temperature regions closely align with regions of high OH-PLIF intensity while the temperature standard deviation better matches the flame-surface density. The signal intensity distribution within instantaneous OH-PLIF images indicates transport of combustion products toward the grain, supported by the near-wall peak temperatures. This process is critical for the transport of energy to the grain such that additional fuel can be volatilized and mix with the air to support the flame.</p><p dir="ltr"><br>Finally, an ultra-fast CARS system has been designed and aligned for 1 kHz one-dimensional measurements of temperature by targeting the ro-vibrational Q-branch transitions of nitrogen. This effort seeks to develop a technique that can capture the hydrodynamics that drive the combustion in SFRJ and provide an intuition for the energy transport near the solid fuel wall of the SFRJ combustor through capturing instantaneous temperature profiles. The designed system utilized a 9 W high-energy regenerative amplifier with 30 fs duration pulses.<br>For the CARS measurement, the 4 W 800 nm output from the external compressor is used as the Stokes beam and a 0.5 W, 675 nm ouput from the TOPAS optical parametric amplifier (OPA) split to and used as the pump and probe beams. A chirping rod placed in the beam path of the probe beam was used to generate the picosecond pulse. Preliminary measurements have been acquired within room air and a laminar H2-Air nonpremixed flame. A discussion of the experimental challenges and remaining work is presented in this document.</p>

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Multiphase machines have gained a lot of popularity during the recent years, as they offer certain benefits over their three- phase counterparts. This work investigates the losses of a six- phase drive system for a marine application when supplied by four different switching patterns (also referred to as modulators). Using MATLAB/SIMULINK, a model was built for the machine featuring three independent frames and a nonlinear, cross coupled flux relation. It has been connected with the corresponding modulator models, where two carrier based modulators have been developed as well as two space vector modulators. The simulation data was then fed into the loss model to calculate the corresponding losses in both machine and converter. This work shows that control of the harmonics – mainly the fifth – is beneficial to reduce losses, mainly in the machine. Without control, harmonic currents cause unnecessary losses. As none of the investigated modulators strictly dominates all others, this work suggests a combination of the two carrier based methods to create a simple and robust modulator, which offers both a high voltage magnitude and control over the fifth harmonic. / Flerfasmaskiner har blivit mycket populära under dem sestate åren, då de erbjuder specifika fördelar jämfört med trefasmaskiner. Denna avhandling undersöker förlusterna för en sexfasmakin för en maritim applikation när den är kopplad med fyra olika modulatorer. En modell för maskinen byggdes i MATLAB/SIMULINK som innehåller tre oberoende nivåer samt en ickelinjär och korskopplad flödesrelation. Maskinmodellen har blivit kopplad till de fyra korresponderande modulatormodellerna, där två är baserade på bärvågor samt de resterande två är baserade på rymdvektormodulation. Data erhållen från den simulerade maskinmodellen var sedan inmatad in i en förlustmodell för att beräkna de korresponderande förlusterna för både maskinen och omvandlare. Denna avhandling visar att kontroll av övertoner – i huvudsak den femte övertonen – är fördelaktigt för att minimera förluster, främst i maskinen. Eftersom ingen av de modulatorerna som undersöktes i avhandlingen är bäst i alla funktioner, föreslår avhandlingen en kombination av de två bärvågsberoende metoderna. Den nya modulatorn antas vara bra både i kontrol över den femte övertonen men också erbjuda en hög spänning tack vare hamonisk injektion. Dessutom är modulatorn enkelt och robust, eftersom det är bärvågberoende.

Multiphase systems provide an opportunity to develop both novel processing methods and create advanced materials through combining the properties of dissimilar phases in a synergistic manner. In this work, we detail the halogenation of poly(ether ether ketone) (PEEK) through both solution-state and gel-state functionalization methods. The multiphase gel-state chemistry restricts functionalization to the amorphous regions of the semi-crystalline parent homopolymer and generates a copolymer with a blocky microstructure. Solution-state functionalization yields random copolymers which provide matched sets to the blocky analogs for fundamental investigations into the effects of polymer microstructure on material properties. Halogenating PEEK using N-halosuccinimides allows for direct installation of pendant halogens along the polymer backbone with facile control of halogen identity. For both bromination and iodination, blocky halogenation of PEEK provides faster crystallization kinetics, higher glass transition (Tg) and melting temperatures as well as superior crystallizability than random halogenation. When comparing halogen identity, increasing halogen size results in increased Tgs, decreased backbone planarity, and for copolymers with blocky microstructures, an earlier onset of phase separation. Increasing halogen size also results in decreased crystallizability and crystallization kinetics, however, these deleterious effects are mitigated in blocky microstructures due to colocalization of the pristine repeat units. Iodination also results in greater flame resistance than bromination for PEEK-based copolymers, and preserved crystallizability allows for the generation of flame retardant aerogels. Direct halogenation of PEEK in the gel-state also provided a reactive microstructural template for subsequent functionalization. Through the use of copper mediated cross-coupling chemistries, the aryl halide functionalities were leveraged to decorate the polymer backbone with pendant perfluoroalkyl chains. The blocky perfluoro alkyl PEEK demonstrated preserved crystallizability and serves as a candidate for compatibilization of poly(tetrafluoroethylene)-PEEK polymer blends. Superacid-modified PEEK was synthesized through a similar methodology and demonstrated over 50,000% increased hygroscopicity relative to the parent homopolymer, and exhibited preserved crystallizability. Multiphase systems were also designed to additively manufacture reinforced elastomers through vat photopolymerization using a degradable scaffold approach that challenged the current paradigm that the scaffold only serves as a geometrical template in vat photopolymerization. The scaffold crosslinks were cleaved through a reactive extraction process that liberated the glassy photopolymer backbone and resulted in over 200% increased ultimate strain and 50% increased ultimate stress relative to a control that was subjected to a neutral extraction. Lastly, thermoresponsive micellar ligands were synthesized as a multiphase approach to environmental remediation of metal-contaminated aqueous systems. / Doctor of Philosophy / Multiphase systems, such as a mixture of oil and water, are of great interest due to their ability to exhibit a multitude of properties from one material. Minimizing the size of the phases, through a technique called compatibilization, often improves the properties of the material. A common example is salad dressing, where the oil phase is compartmentalized into microscopic particles using surface-active molecules known as surfactants. Surfactants, also known as amphiphiles, partition to the interface between different phases due to the surfactants being comprised of dissimilar molecular constituents. One way to generate polymeric amphiphiles, where a polymer is a large molecule comprised of a molecular chain of repeating units, is through synthesizing block copolymers. Block copolymers have blocks of different constituents that are colocalized through covalent bonds in the polymer backbone and often exhibit phase separated structures, allowing for enhanced transport properties such as is seen in membranes. Using semi-crystalline polymers in membranes allows for enhanced mechanical integrity, as the crystallites act as physical crosslinks, or tie points, similar to the knots in a 3D rope ladder. These molecular knots limit the distance that the linear segments of the rope ladder can stretch, which in membranes leads to reduced swelling and increased mechanical performance. In this work we use semi-crystalline polymers to generate blocky copolymers through the use of halogenation. Halogenation installs halogen moieties as pendant groups on the polymer backbone, which can then by used as a chemical handle for subsequent reactions to further incorporate functionality into the copolymer and achieve desired properties such as proton (hydrogen nuclei) transport in fuel cell membranes. Halogenation also allows for the generation of blocky semi-crystalline copolymers for compatibilizing polymer blends of materials like poly(tetrafluoroethylene) and poly(ether ether ketone). Also in this work, we discuss the additive manufacturing of mechanically reinforced elastomers. An elastomer is another type of crosslinked network, and a mechanically reinforced elastomer can be through of as a 3D rope ladder where some of the linear segments of rope are replaced with steel bars, thus increasing the amount of work required to deform the network. The last multiphase systems discussed are similar to salad dressing, where there is a continuous water phase and a microscopic particle phase. The microscopic particles in this work are amphiphilic block copolymers that change their solubility in water with temperature and also have functionalities that should allow for the binding of metals from water-based systems.

Modelling solid-fluid multiphase flows are crucial to many applications such as fluidized beds, pyrolysis and gasification, catalytic cracking etc. Accurate modelling of the fluid-particle forces is essential for lab-scale and industry-scale simulations. Fluid-particle system solutions can be obtained using various techniques including the macro-scale TFM (Two fluid model), the meso-scale CFD-DEM (CFD - Discrete Element Method) and the micro-scale PRS (Particle Resolved Simulation method). As the simulation scale decreases, accuracy increases but with an exponential increase in computational time. Since fluid forces have a large impact on the dynamics of the system, this study trains deep learning models using micro-scale PRS data to predict drag forces on ellipsoidal particle suspensions to be applied to meso-scale and macro-scale models. Two different deep learning methodologies are employed, multi-layer perceptrons (MLP) and 3D convolutional neural networks (CNNs). The former trains on the mean characteristics of the suspension including the Reynolds number of the mean flow, the solid fraction of the suspension, particle shape or aspect ratio and inclination to the mean flow direction, while the latter trains on the 3D spatial characterization of the immediate neighborhood of each particle in addition to the data provided to the MLP. The trained models are analyzed and compared on their ability to predict three different drag force values, the suspension mean drag which is the mean drag for all the particles in a given suspension, the mean orientation drag which is the mean drag of all particles at specific orientations to the mean flow, and finally the individual particle drag. Additionally, the trained models are also compared on their ability to test on data sets that are excluded/hidden during the training phase. For instance, the deep learning models are trained on drag force data at only a few values of Reynolds numbers and tested on an unseen value of Reynolds numbers. The ability of the trained models to perform extrapolations over Reynolds number, solid fraction, and particle shape to predict drag forces is presented. The results show that the CNN performs significantly better compared to the MLP in terms of predicting both suspensions mean drag force and also mean orientation drag force, except a particular case of extrapolation where the MLP does better. With regards to predicting drag force on individual particles in the suspension the CNN performs very well when extrapolated to unseen cases and experiments and performs reasonably well when extrapolating to unseen Reynolds numbers and solid fractions. / M.S. / Multiphase solid-fluid flows are ubiquitous in various industries like pharmaceuticals (tablet coating), agriculture (grain drying, grain conveying), mining (oar roasting, mineral conveying), energy (gasification). Accurate and time-efficient computational simulations are crucial in developing and designing systems dealing with multiphase flows. Particle drag force calculations are very important in modeling solid-fluid multiphase flows. Current simulation methods used in the industry such as two-fluid models (TFM) and CFD-Discrete Element Methods (CFD-DEM) suffer from uncertain drag force modeling because these simulations do not resolve the flow field around a particle. Particle Resolved Simulations (PRS) on the other hand completely resolve the fluid flow around a particle and predict very accurate drag force values. This requires a very fine mesh simulation, thus making PRS simulations many orders more computationally expensive compared to the CFD-DEM simulations. This work aims at using deep learning or artificial intelligence-based methods to improve the drag calculation accuracy of the CFD-DEM simulations by learning from the data generated by PRS simulations. Two different deep learning models have been used, the Multi-Layer Perceptrons(MLP) and Convolutional Neural Networks(CNN). The deep learning models are trained to predict the drag forces given a particle's aspect ratio, the solid fraction of the suspension it is present in, and the Reynolds number of the mean flow field in the suspension. Along with the former information the CNN, owing their ability to learn spatial data better is additionally provided with a 3D image of particles' immediate neighborhood. The trained models are analyzed on their ability to predict drag forces at three different fidelities, the suspension mean drag force, the orientation mean drag, and the individual particle drag. Additionally, the trained models are compared on their abilities to predict unseen datasets. For instance, the models would be trained on particles of an aspect ratio of 10 and 5 and tested on their ability to predict drags of particles of aspect ratio 2.5. The results show that the CNN performs significantly better compared to the MLP in terms of predicting both suspension mean drag force and also mean orientation drag force, except a particular case of extrapolation where the MLP does better. With regards to predicting drag force on individual particles in the suspension, the CNN performs very well when extrapolated to unseen cases and experiments and performs reasonably well when extrapolating to unseen Reynolds numbers and solid fractions.

<p dir="ltr">Results from the Flow Boiling and Condensation Experiment (FBCE), which collected the first flow boiling and condensation data in long-duration, steady microgravity through experiments performed onboard the International Space Station (ISS), are presented. Prior to the ISS experiments, a new correlation for flow boiling critical heat flux (CHF) is developed from data obtained in Earth gravity at different orientations and short durations of microgravity onboard parabolic flight. The new correlation accounts for the influence of gravity in the direction of the flow, impacting vapor removal from the channel, and perpendicular to the heated walls, affecting bubble detachment from the walls, on flow boiling CHF. Novel flow boiling experiments in long-duration microgravity were performed with one or two opposite walls heated using the Flow Boiling Module (FBM), which simultaneously captures heat transfer data and high speed images of flow patterns. The unique microgravity CHF results are presented, and parametric trends are correlated to variations in flow patterns. The results are divided into subcooled and saturated inlet conditions and applicable correlations are assessed. The newly proposed correlation outperforms is the best preforming for the entire database, validating its use in microgravity. Visual observations leading up to CHF justify use of the Interfacial Lift-off model, which predicts CHF with good accuracy for all operating conditions. The data obtained onboard the ISS is consolidated with the prelaunch database to develop highly accurate artificial neural networks (ANNs) for flow boiling heat transfer and CHF in microgravity. The ANNs are developed using a systematic approach that enables the prediction of physical trends. Instabilities observed during subcooled flow boiling are further investigated in dedicated experiments performed at an elevated data capture rate of 30 Hz and extended image capture period up to 28 s. Criteria was proposed to demarcate the stable and unstable operating conditions, and a new correlation to predict the onset of flow instability is proposed. Lastly, microgravity flow condensation heat transfer experiments were conducted onboard the ISS, yielding the first flow condensation data in stable microgravity. Trends in the data are discussed and the two-phase mixture Reynolds number is found to be strongly correlated to local heat transfer coefficient. A separated flow model for annular flow is found to accurately predict trends in average heat transfer coefficient, but underpredicts the microgravity database.</p>