Assessment of low- and high-fidelity turbulence models for heat transfer predictions in low-prandlt number flows

Abstract

Linear eddy viscosity-based Reynolds-averaged Navier-Stokes (RANS), hybrid RANS/Large Eddy Simulation (LES), and LES models are applied for simulations of plane channel flow at Reynolds number Reτ = 150 and 640. Results are obtained for fluids with Prandtl number (Pr) of 0.025 and 0.71, for cases without buoyancy (Gr = 0) and for a vertical channel flow with buoyancy (Ri = 0.1–0.13). The objective is to evaluate the predictive capability of each class of model for low-Pr flow typical of liquid metal reactor cooling systems. For this purpose, RANS, partially-averaged Navier-Stokes (PANS) and LES computations are performed on grids which contain 0.67%, 2%, and 5% of the grid points used in the Direct Numerical Simulation (DNS), respectively, and the predictions of the mean and turbulent flow and thermal quantities are validated against DNS results. RANS results obtained using Kays variable turbulent Prandtl number (PrT) formulation were more accurate than those using constant PrT for low-Pr forced convective flows, but did not play a significant role for mixed convective flows. The variable PrT formulation likewise did not have significant effect for hybrid RANS/LES or LES computations, wherein > 90% turbulence was resolved. The RANS model performed reasonably well for flows without buoyancy, but for mixed convective flows, significantly under predicted the sharp pressure gradient around the mid-channel, and the limitations were identified due to over prediction of heat transfer by wall-normal fluctuations on the aiding side. Both PANS and dynamic Smagorinsky (DSM) showed similar predictions, and under predicted the mid-channel temperature gradient for the low-Pr vertical channel case and over predicted flow turbulence on the opposing flow side and thermal fluctuations on the aiding flow side. The Wall-Adapting Local Eddy-viscosity (WALE) model performed best among the LES subgrid stress models. The improved predictions by WALE over PANS and DSM were identified because of higher modeled dissipation predictions away from the wall. Future work will focus on investigation of non-linear RANS models and extend the validation for higher Re flows.