Unsteady LES and RANS Computations of Natural Convection Cooling Loops

Natural Circulation Loops (NCLs), where fluid is driven through a closed circuit solely by thermal imbalance, offer potential for use in passive cooling systems within nuclear power plants. The resulting flows are three dimensional and the combination of uniform heat flux at the hot end and uniform temperature at the cold side promotes thermal imbalances which can cause large-scale flow oscillations, including flow reversal. Moreover, the range of operating conditions extends from the laminar to the transitional and turbulent regions. There are consequently severe challenges
to the reliable numerical simulation of this phenomenon. This study employs both unsteady RANS models and Large Eddy Simulation (LES) for the conjugate heat transfer analysis of two experimental NCLs across a range of different heating conditions and input parameters. First, the LES was used to provide a high-fidelity reference computation with which to compare the performance of a number of widely used RANS models (Launder-Sharma k − ε, k − ω SST and Elliptic-Blending RSM). The results indicate that the LS k − ε provides reasonable agreement when compared to the more advanced EB-RSM model and at a reduced computational cost. The k − ω SST model performed less well, tending to overpredict turbulence levels within the loop, leading to significantly cooler temperatures, higher heat transfer coefficients and flatter velocity profiles. Second, the subsequent application of the LS k − ε model to a range
of heating configurations, heater powers and cooler temperatures demonstrated good agreement with the experimental data, correctly capturing the trends presented by both the experimental data and existing correlations. Overall, the results demonstrate the suitability of the unsteady RANS approach in modelling flow behaviour typical of NCLs and contrast the performance of two widely used modelling approaches, providing insight for those designing, optimizing or experimenting with NCLs.