<div> <p> Aircraft engines require lightweight efficient thermal management devices to improve engine performance at high pressure ratios. Acoustic streaming can provide a viable, lightweight solution to improve the heat exchanger capacity with a reduced drag penalty within engine heat exchangers. This project develops a test section that will experimentally characterize the effect of acoustic streaming on the unsteady heat flux and shear stress within a narrow channel. This is accomplished by careful selection of measurement techniques to monitor the steady and unsteady properties of the flow and iteratively designing the test section with CFD support to converge to an optimal test model. Using CFD support to revise each iteration reduces the experimental cost of developing an effective geometry. </p> <p> Pressure taps and K-type thermocouples are used to monitor the total inlet pressure and temperature as well as the wall surface pressure and temperature. Optical shear stress sensors are selected to monitor the unsteady wall shear stress. A thin film sensor array is designed for high frequency wall temperature measurements which serve the boundary condition for a 1-D heat flux analysis to determine the unsteady heat flux through the wall. The test model consists of two hollow Teflon airfoils that create a narrow channel within a larger flow area. The airfoils create three flow paths within the wind tunnel test section and the area ratio between the measured flow and the bypass flow controls the Mach number of within the measured flow channel. The acoustic waves drive acoustic streaming and are generated by a Rossiter Cavity with L/D =2 which produces pressure oscillations with dominant frequency of 8 kHz in a Mach 0.8 flow. </p> <p> The test geometry successfully achieves <a>Mach 0.8 flow and the 8 kHz signal </a><a href="https://purdue0-my.sharepoint.com/personal/mbutzen_purdue_edu/Documents/MS Thesis/Thesis Living Document.docx#_msocom_1">[BMJW1]</a> from the Rossiter cavity. The successful commissioning sets the stage for future experiments to determine the potential of acoustic streaming as a low weight modification to improve compact heat exchangers. </p> </div> <div><div><div><br> </div> </div> </div>

<p dir="ltr">As a means to address the imminent and severe threat of global climate change, the emissions of the transportation sector must be addressed in the near term. Harnessing limited computational resources in an efficient manner is paramount to improving our existing internal combustion (IC) systems; however, low fidelity models have been shown to have difficulty accurately representing a highly turbulent combustor, such as in Turbulent Jet Ignition (TJI). TJI replaces the spark ignition source of a conventional IC system with a small combustion chamber that injects hot ignition products into the main combustion chamber. This process reduces the probability of engine misfires, particularly at the ultra-lean condition, improving the thermal efficiency of existing IC systems. This study develops a framework for the training and validation of a Deep Neural Network (DNN) model for the prediction of chemical reaction rates as a means to address the poor performance of existing low-fidelity combustion models for TJI. A high-fidelity combustion simulation was performed under the large eddy simulation (LES) equations with a transported probability density function (PDF) combustion model, and validated against experimental results gathered from a single-cycle TJI rig performed by a research group at Purdue University. This simulation data was used to train a DNN model to predict the turbulent reaction rates for a 19-species, 84-reaction reduced order reaction mechanism for the combustion of methane. A supervised learning approach involving stochastic gradient descent with backpropagation was used to optimize the DNN model; additionally, a self-organizing map (SOM) was used to cluster the model input into burnt, unburnt and reacting regimes. The framework was then examined in three cases: initial a-priori validation against the LES-PDF training set, a-posteriori validation of a single-cycle test apparatus developed at Purdue University (Purdue TJI rig), and a-posteriori validation of a single-cylinder engine developed at Argonne National Laboratory (Argonne TJI engine). The study found that the a-priori performance of the model was dependent on the state of the SOM cluster, as the framework was only able to accurately predict species that were active in a given cluster. The framework was found to capture the ignition in the Purdue TJI rig under a low-fidelity Reynolds averaged Navier Stokes (RANS) simulation, while a traditional laminar finite rate chemistry (LFRC) model was unable to capture this event. When applied to the Argonne TJI engine, the framework outperformed the common SAGE model that was unable to capture the pressure rise seen in experimental results. However, the performance of the model was limited by the availability of simulation data, and there exists room for improvement in the model.</p>

<p dir="ltr">Evidence for climate change-related impacts and risks is already widespread globally, affecting not only the ecosystems but also the economy and health of our communities. Data-driven predictive modeling approaches such as machine learning and deep learning have emerged to be powerful tools for interpreting large and complex non-linear datasets such as meteorological variables from weather stations or the distribution of infectious droplets produced in a cough. However, the strength of these data-driven models can be further optimized by complementing them with foundational knowledge of the physical processes they represent. By understanding the core physics, one can enhance the reliability and accuracy of predictive outcomes. The effectiveness of these combined approaches becomes particularly feasible and robust with the recent advancements in the High-Performance Computing field. With improved processing speed, algorithm design, and storage capabilities, modern computers allow for a deeper and more precise examination of the data. Such advancements equip us to address the diverse challenges presented by climate change more effectively.</p><p dir="ltr">In particular, this document advances research in mitigating and preventing the consequences of global warming by implementing data-driven predictive models based on statistical, machine learning, and deep learning methods via two phases. In the first phase, this dissertation proposes frameworks consisting of machine and deep learning algorithms to increase the resilience of small-scale renewable energy systems, which are essential for reducing greenhouse gas emissions in the ecosystems. The second phase focuses on using data from physics-based models, i.e., computational fluid dynamics (CFD), in data-driven predictive models for improving the design of air cleaning technologies, which are crucial to reducing the transmission of infectious diseases in indoor environments. </p><p dir="ltr">Specifically, this work is an article-based collection of published (or will be published) research articles. The articles are reformatted to fit the thesis's structure. The contents of the original articles are self-contained. </p>

<p>In this study, universal databases and semi-empirical correlations are developed for cryogenic two-phase heat transfer and pressure drop in heated tubes undergoing flow boiling.</p>

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

<p dir="ltr">Tip leakage flows in unshrouded turbines lead to significant pressure losses and heat loads, both on the rotating blades and the adjacent casing surface. These penalties are influenced by the tip clearance size, highly pertinent to the new generation of small-core high-speed turbines. Tailored to decrease tip leakage effects, small-core turbines feature running clearances below 0.3mm, making small blade-to-blade clearance variations extremely relevant for the machine's performance. Therefore, tip clearance monitoring and assessment of the leakage flow structures are paramount to design strategies for this class of turbines. Due to the limitations of commercially available CFD tools to accurately resolve highly detached unsteady flows, in-situ empirical observations are required. Furthermore, the documentation of flow field relationships with the tip clearance is highly valuable for in-service engine applications, such as tip clearance estimations from more accessible measurements to provide feedback for clearance control systems.</p><p dir="ltr">The dissertation developed hereafter performs aerothermal measurements in the casing end wall of a small-core high-speed turbine at engine-representative conditions and a wide range of clearance values. A novel in-situ calibration procedure for capacitance probes is tailored to reduce the required clearance measurements and the experimental time. Its uncertainty analysis demonstrates improved prediction bands, supporting this method for tight clearance measurements. A thorough evaluation of the casing static pressure is performed with high-frequency miniature pressure transducers. Specific trends are identified with independent variations of operating pressure ratio, rotational speed, and tip clearance. The results revealed the existence of a clearance-dependent threshold rotational blade tip Reynolds, where the circumferential directionality of tip leakage flows reverses. The analysis of the convective heat flux field with varying operating parameters was achieved with Atomic Layer Thermopile sensors. The computed adiabatic parameters and unsteady contributors reveal high influence of the temperature field on the convective heat flux mechanisms. Lastly, the evaluation of the unsteady terms with tip clearance unveil the shift of thermal loads from the pressure to the suction side of the blade tip.</p><p dir="ltr">The achieved results have provided valuable insight into the underlying aerothermal mechanisms governing the tip clearance region, as well as connections with tip clearance size that could potentially be implemented on engine application systems.</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">Positive displacement machines are used in various industries spanning the power spectrum, from industrial robotics to heavy construction equipment to aviation. These machines should be highly efficient, compact, and reliable. It is very advantageous for designers to use virtual simulations to design and improve the performance of these units as they significantly reduce cost and downtime. The recent trends of electrification and the goal to increase power density force these units to work at higher pressures and higher rotational speeds while maintaining their efficiencies and reliability. This push means that the simulation models need to advance to account for various aspects during the operation of these machines. </p><p dir="ltr">These machines typically have several bodies in relative motion with each other. Quantifying these motions and solving for their effect on the fluid enclosed are vital as they influence the machine's performance. The push towards higher rotational speeds introduces unwanted cavitation and aeration in these units. To model these effects, keeping the design evaluation time low is key for a designer. The lumped parameter approach offers the benefit of computational speed, but a major drawback that comes along with it is that it typically assumes fluid inertia to be negligible. These effects cannot be ignored, as quantifying and making design considerations to negate these effects can be beneficial. Therefore, this thesis addresses these key challenges of cavitation dynamics, body dynamics, and accounting for fluid inertia effects using a lumped parameter formulation.</p><p dir="ltr">To account for dynamics features related to cavitation, this thesis proposes a novel approach combining the two types of cavitation, i.e., gaseous and vaporous, by considering that both vapor and undissolved gas co-occupy a spherical bubble. The size of the spherical bubble is solved using the Rayleigh-Plesset equation, and the transfer of gas through the bubble interface is solved using Henry's Law and diffusion of the dissolved gas in the liquid. These equations are coupled with a novel pressure derivative equation. To account for body dynamics, this thesis introduces a novel approach for solving the positions of the bodies of a hydraulic machine while introducing new methods to solve contact dynamics and the application of Elasto Hydrodynamic Lubrication (EHL) friction at those contact locations. This thesis also proposes strategies to account for fluid inertia effects in a lumped parameter-based approach, taking as a reference an External Gear Machine. This thesis proposes a method to study the effects of fluid inertia on the pressurization and depressurization of the tooth space volumes of these units. The approach is based on considering the fluid inertia in the pressurization grooves and inside the control volumes with a peculiar sub-division. Further, frequency-dependent friction is also modeled to provide realistic damping of the fluid inside these channels.</p><p dir="ltr">To show the validity of the proposed dynamic cavitation model, the instantaneous pressure of a closed fluid volume undergoing expansion/compression is compared with multiple experimental sources, showing an improvement in accuracy compared to existing models. This modeling is then further applied to a gerotor machine and validated with experiments. Integrating this modeling technique with current displacement chamber simulation can further improve the understanding of cavitation in hydraulic systems. Formulations for body dynamics are tested on a prototype Gerotor and Vane unit. For both gerotor and vane units, comparisons of simulation results to experimental results for various dynamic quantities, such as pressure ripple, volumetric, and hydromechanical efficiency for multiple operating conditions, have been done. Extensive validation is performed for the case of gerotors where shaft torque ripple and the motion of the outer gear is experimentally validated. The thesis also comments on the distribution of the different torque loss contributions. The model for fluid inertia effects has been validated by comparing the lumped parameter model with a full three-dimensional Navier Stokes solver. The quantities compared, such as tooth space volume pressures and outlet volumetric flow rate, show a good match between the two approaches for varying operating speeds. A comparison with the experiments supports the modeling approach as well. The thesis also discusses which operating conditions and geometries play a significant role that governs the necessity to model such fluid inertia effects in the first place.</p>