<p>The purpose of this research was to determine what components of a complex centrifugal  compression system inlet needed to be modelled to accurately predict the swirl and total pressure  distortions at the compressor face. Two computational models were developed. A full-fidelity case  where all the inlet geometry was modelled and a reduced model where a small portion of the inlet  was considered. Both the numerical cases were compared with experimental data from a research  compressor rig developed by Honeywell Aerospace. The test apparatus was designed with a  modular inlet system to develop swirl distortion patterns. The modular inlet system utilized  transposable baffles within the radial-to-axial section of the inlet and blockage plates of varying  sizes and geometries at the inlet to this section.  Discerning the dominant inlet component that dictates distortion behavior at the compressor  face would allow the reduced modelling of inlet components for compression systems and would  allow coupling with more tortuous systems. Furthermore, it would reduce the design iteration and  simulation time of the inlet systems. Several investigations utilizing a reduced model only  considering a radial-to-axial inlet are available in literature, but no comprehensive justification has  been presented as to the impact this has on the distortion behavior.   Experimental surveys of flow conditions just upstream of the inducer of the centrifugal  compressor were conducted at several operating conditions. The highest and lowest mass flow  rates of these operating points were simulated using ANSYS CFX 2020R1 for both the  computational models. Multiple inlet configurations were simulated to test the robustness of the  reduced model in comparison to the full fidelity. The numerical simulations highlighted  shortcomings of the instrumentation used to characterize the experimental flow field at the inducer,  particularly with respect to total pressure distortion. Furthermore, transient pressure data were  measured in experiment and indicated unsteady fluctuations in the inlet that would not be captured  by steady computational fluid dynamic simulations. These data matched locations of disagreement  with swirl distortion behavior at high mass flow rates. This suggested that transient vortex  movement occured at the aerodynamic interface plane in certain configurations.   The total pressure distortion metrics between the two models were remarkably comparable.  Furthermore, the simplified model accurately predicted the mixing losses associated with the  blockage plates at the inlet to the radial-to-axial inlet using a simple inlet extension. Swirl  18  distortion was dictated by the radial-to-axial inlet. The reduced model data trends were comparable  with experiment for both the baffle and blocker plate configurations. The swirl intensities for all  configurations were comparable between the two models. The reduced model swirl directivity  trends matched those of experiment. The most notable deviations between the full-fidelity model  and the reduced model were observed with swirl directivity numerics. </p>

<p>The goal of this thesis is to algorithmically identify candidate modifiers for <i>retinitis pigmentosa</i> (RP) to help improve therapy and predictions for this genetic disorder that may lead to a complete loss of vision. A current research by (Chow et al., 2016) focused on the genetic contributors to RP by trying to recognize a correlation between genetic modifiers and phenotypic variation in female <i>Drosophila melanogaster</i>, or fruit flies. In comparison to the genome-wide association analysis carried out in Chow et al.’s research, this study proposes using a K-Means clustering algorithm on RNA expression data to better understand which genes best exhibit characteristics of the RP degenerative model. Validating this algorithm’s effectiveness in identifying suspected genes takes priority over their classification.</p><p>This study investigates the linear relationship between <i>Drosophila </i>eye size and genetic expression to gather statistically significant, strongly correlated genes from the clusters with abnormally high or low eye sizes. The clustering algorithm is implemented in the R scripting language, and supplemental information details the steps of this computational process. Running the mean eye size and genetic expression data of 18,140 female <i>Drosophila</i> genes and 171 strains through the proposed algorithm in its four variations helped identify 140 suspected candidate modifiers for retinal degeneration. Although none of the top candidate genes found in this study matched Chow’s candidates, they were all statistically significant and strongly correlated, with several showing links to RP. These results may continue to improve as more of the 140 suspected genes are annotated using identical or comparative approaches.</p>