The Icing Research Tunnel at NASA Glenn has recently switched from using the Icing Blade to using the SEA Multi-Element Sensor (also known as the multi-wire) for its calibration of cloud liquid water content. In order to peform this transition, tests were completed to compare the Multi-Element Sensor to the Icing Blade, particularly with respect to liquid water content, airspeed, and drop size. The two instruments were found to compare well for the majority of Appendix C conditions. However, it was discovered that the Icing Blade undermeasures when the conditions approach the Ludlam Limit. This paper also describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, mounting underneath a splitter plate, and correcting for a jump in the compensation wire power. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor, health-monitoring checks for the instrument, and a sensingelement configuration comparison. Ultimately these tests showed that in the IRT, the multiwire is a better instrument for measuring cloud liquid water content than the blade.

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