Three-dimensional Reynolds-Averaged Navier-Stokes (RANS) simulations using theMenter-SST turbulence model were performed on the new 2012 configuration of the NASA Glenn Icing Research Tunnel (IRT). The IRT was simulated from the exit of the heat exchanger to the test section. A two-dimensional simulation was first performed on a crosssection of the heat exchanger to provide initial conditions for three-dimensional flow predictions through the turning vanes, spray bars, tunnel contraction and test section. The simulations showed a general increase in turbulence intensity within the test section as compared to previous IRT configurations (in 2000 and 2009) which can be attributed to wake effects from both the heat exchanger and spray bars. In addition, the heat exchanger produced variations in the yaw flow angles after the turning vanes which are consistent with experiments while the corner geometry resulted in higher flow turbulence gradients for the inner wall regions. Using these simulated time-averaged flowfields, droplet trajectories were predicted using Lagrangian calculations with an unsteady Discrete Random Walk (DRW) model to mimic turbulent fluctuations. A transfer map was developed by mapping the water droplet locations at the test section by tracking peak concentration contours associated with both nozzle rows and nozzle columns. In addition, the liquid water concentration (LWC) distribution at the test section was predicted using the 2012 calibrated nozzle locations. The simulated transfer map and LWC distribution were both qualitatively similar to experiments but demonstrate that the RANS model may not be capturing unsteadiness associated with the spray bar wakes and the jets. An appendix was provided with data on a hybrid RANS/LES simulation of a section of the IRT which captured the unsteady behavior of the airflow. The hybrid model predicts significantly higher turbulence in the spray bar wake than the RANS model which would explain the disparities between with the droplet trajectory calculations and experiments. Nomenclature α = liquid water concentration C(d) = cumulative distribution function d = droplet diameter d rr = Rosin-Rammler reference diameter h rr = Rosin-Rammler spread parameter k = turbulent kinetic energy k av = average turbulent kinetic energy l = turbulent length scale u = air flow velocity v = water droplet velocity ω = specific dissipation rate x = streamwise distance y = vertical distance y+ = dimensionless wall distance z = spanwise distance

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This thesis develops a methodology for the design phase of a Prognostics Health Management (PHM) system of a manufacturing process. The methodology (i) builds upon Hierarchical Holographic Modeling (HHM), Risk Filtering, Ranking, and Management (RFRM), and Fault Tree Analysis (FTA); (ii) provides scope and direction for a PHM system by identifying a prioritized set of targets that would most benefit from PHM capabilities; and (iii) tests the outcome of the developed methodology with two case studies with U.S. manufacturing companies. Currently, there are multiple methods to determine the major failure modes of a system following an accident or catastrophe. However, the proposed methodology in this thesis allows for a thorough analysis to be conducted even before a failure occurs in a manufacturing environment. Another important goal of this thesis is to demonstrate the compatibility and synergy between two seemingly different methodologies/processes: Risk analysis and PHM for manufacturing. More specifically, this research demonstrates that the theory, methodology, and current practice of the system-based risk analysis are harmonious and compatible with PHM.