Nuclear energy is a promising alternative to fossil fuel energy sources. With advances to current cooling technology, nuclear energy can achieve more energy production and run more efficiently than earlier reactor designs. Advances in cooling technology require new coolants, and for the nuclear industry one option for these come in the form of liquid metals. Liquid metals have potential to substantially improve cooling performance, however the behavior of such fluids has not been studied in depth due to the difficulties that lie in experimenting with these fluids. With computational advances, simulation is often the best option for predicting fluid flows. Due to the low Prandtl number (Pr) of liquid metals, modeling is somewhat challenging as traditional models do not accurately predict the turbulent heat transfer behavior of low Pr fluids. Although some research has been conducted for low Pr fluid simulation, the answer to which model to use for these fluids is not entirely clear. This document seeks to implement traditional eddy viscosity RANS models within a CFD simulation code and evaluate them based on their ability to accurately simulate simple heat transfer processes involving low Pr fluids. The study also seeks to quantify the potential improvement of Kays formulation, a turbulent thermal diffusivity modification, within those models. Computational simulations were performed for channel flow, backward facing step flow, and a simple rod bundle geometry to test the applicability and validity of these models. Simulations were run for values of Pr comparable to air like fluids and low Pr comparable to liquid metals, and all results were compared to DNS or experimental data available for the test cases selected. The results of this study show that typical unmodified k-ε models do not consistently provide accurate results for low Pr fluid flows. The addition of Kay’s formulation shows a general improvement on the baseline models. More complex models may benefit more than the simple models tested here and improve results by implementing Kay’s formulation.