This thesis presents results of computations of supersonic flow over finite cylinders with varying geometries at the cylinder-wall juncture. The flow domain and geometries were modeled after experiments conducted at University of Tennessee Space Institute (UTSI). CREATE Kestrel (KCFD) was used to perform improved-delayed detached simulations (IDDES) of the unsteady flow. Time-accurate data were collected via taps along the centerline partially on the surface of the cylinder geometries and on the wall upstream of the cylinder. Spectra of the pressure signals and two-point correlations were computed to compare the flow between the different cases consisting of a baseline cylinder, the cylinder with a smaller gap, and the cylinder with a wider fairing. Properties on the cylinder surface for the gap case had the greatest difference compared to the others. In addition, the spectral content showed higher frequency activity for the gap case on the surface in front of the cylinder. <br>

<p dir="ltr">Increased scrutiny from customers and regulators to design aeroengines that are more efficient and environmentally friendly has pushed the need to investigate new engine architectures, manufacturing techniques, and computational fluid dynamic methods. This has led to the development of the CSTAR Gen. 2.5 centrifugal compressor, which uses an additively manufactured diffusion system and investigates the aerodynamic performance of an axi-centrifugal aeroengine. Additionally, an isolated unsteady diffuser computational model was previously developed that seeks to significantly reduce the computational cost of unsteady CFD in the diffuser.</p><p dir="ltr">The research presented in this paper is part of an ongoing attempt to utilize the capabilities of isolated unsteady diffuser modeling and rapid prototyping enabled through additive manufacturing in CSTAR Gen. 2.5 to develop a design framework that allows for quick computational and experimental analysis of diffusion systems in centrifugal compressors. Specifically, the scope of isolated unsteady diffuser modeling, which was previously only implemented in CSTAR Gen. 1 and at a single loading condition, is expanded by analyzing computational instabilities when applying the methodology to CSTAR Gen. 2.5 and analyzing results from four loading conditions (high loading, design point, low loading, and near choke) along a speedline.</p><p dir="ltr">Computational instabilities in the CSTAR Gen. 2.5 isolated diffuser models were determined to be caused by the decreased vaneless space compared to Gen. 1, which led to less “mixed” flow at the impeller outlet and a stronger diffuser potential field affecting the inlet profile. A boundary profile correction approach was developed which slightly increased very low total pressure near the diffuser shroud and negative radial velocity regions near the shroud and pitchwise locations of the diffuser vane leading edges while minimizing the overall affected area. The correction was successfully validated using 3D flow structure and minimum, average, and maximum total pressure, absolute velocity magnitude, and pressure comparisons at the diffuser inlet between an isolated and full-stage model.</p><p dir="ltr">Prediction capabilities of 3D flow structures and 1D performance parameters by isolated unsteady diffuser models were validated with results from full-stage unsteady models at each loading condition. The analysis indicated consistent performance by the isolated unsteady diffuser model at all loading conditions. An overall agreement in 3D flow structures was found, and trends in the full-stage unsteady models along the speedline were tracked well by the isolated unsteady model. At all loading conditions, there was a consistent over-representation of the separation region along the diffuser vane pressure side in the diffuser passage, overprediction of total pressure magnitude at the core of the flow at the diffuser outlet, and over- or underprediction of total pressure loss and static pressure recovery respectively. The similarity in the results between full-stage and isolated unsteady models, tracking of trends along the speedline, and consistent differences in 3D flow structure predictions and 1D performance parameters validates the isolated unsteady diffuser methodology for use at loading conditions from surge to choke.</p>

Accuracy in turbulence modeling remains a hurdle in the widespread use of Computational Fluid Dynamics (CFD) as a tool for furthering fluids dynamics research. Meanwhile, computational power remains a significant concern for solving real-life wall-bounded flows, which portray a wide range of length and time scales. The tools for turbulence analysis at our disposal, in the decreasing order of their accuracy, include Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and Reynolds-Averaged Navier Stokes (RANS) based models. While DNS and LES would remain exorbitantly expensive options for simulating high Reynolds number flows for the foreseeable future, RANS is and continues to be a viable option utilized in commercial and academic endeavors. In the first part of the present work, flow over the back-step test case was solved, and parametric studies for various parameters such as re-circulation length (X_r), coefficient of pressure (C_p), and coefficient of skin friction (C_f) are presented and validated with experimental results. The back-step setup was chosen as the test case as turbulent modeling of flow past backward-facing step has been pivotal to understand separated flows better. Turbulence modeling is done on the test case using RANS (k-ε and k-ω models), and LES modeling, for different values of Reynolds number (Re ∈ {2, 2.5, 3, 3.5} × 10⁴) and expansion ratios (ER ∈ {1.5, 2, 2.5, 3}). The LES results show good agreement with experimental results, and the discrepancy between the RANS results and experimental data was highlighted. The results obtained in the first part reveal a pattern of under-prediction noticed with using RANS-based models to analyze canonical setups such as the backward-facing step. The LES results show close proximity to experimental data, as mentioned above, which makes it an excellent source of training data for the machine learning analysis outlined in the second part. The highlighted discrepancy and the inability of the RANS model to accurately predict significant flow properties create the need for a better model. The purpose of the second part of the present study is to make systematic efforts to minimize the error between flow properties from RANS modeling and experimental data, as seen in the first part. A machine learning model was constructed in the second part of the present study to predict the eddy viscosity parameter (μt) as a function of turbulent kinetic energy (TKE) and dissipation rate (ε) derived from LES data, effectively working as an ad hoc eddy-viscosity based turbulence model. The machine learning model does not work well with the flow domain as a whole, but a zonal analysis reveals a better prediction of eddy viscosity than the whole domain. Among the zones, the area in the vicinity of the re-circulation zone gives the best result. The obtained results point towards the need for a zonal analysis for the better performance of the machine learning model, which will enable us to improve RANS predictions by developing a reduced order turbulence model.