Assessing the Applicability of the Structure-Based Turbulence Resolution Approach to Nuclear Safety-Related Issues

Abstract

The accuracy of computational fluid dynamics (CFD) predictions plays a fundamental role in supporting the operation of the current nuclear reactor fleet, and even more importantly the licensing of advanced high-efficiency reactor concepts, where local temperature oscillations driven by thermal striping, cycling and stratification can limit the structural performance of vessels and components. The complexity of the geometrical configurations, coupled to the long operational transients, inhibits the adoption of large eddy simulation (LES) methods, mandating the acceptance of the more efficient Reynolds-averaged Navier-Stokes (RANS)-based models, even though they are unable to provide a complete physical description of the flow in regions dominated by complex unsteady coherent structures. A new strategy has been proposed and demonstrated at Massachusetts Institute of Technology (MIT) toward the enhancement of unsteady Reynolds-averaged Navier-Stokes (URANS) predictions, using local resolution of coherent turbulence, to provide higher fidelity modeling in support of safety-related issues. In this paper, a comprehensive assessment of the recently proposed Structure-based (STRUCT-ε) turbulence model is presented, starting from fundamental validation of the model capabilities and later focusing on a representative safety-relevant application, i.e., thermal mixing in a T-junction. Solutions of STRUCT-ε, the widely used Realizable k−ε model (RKE) and Large Eddy Simulation with Wall-Adapting Local Eddy-viscosity subgrid scale closure (LES-WALE) are compared against the experimental data. Both the velocity and temperature fields predicted by the STRUCT-ε model are in close agreement with the high-fidelity data from the experiments and reference LES solutions, across all validation cases. The approach demonstrates the potential to address the accuracy requirements for application to nuclear safety-related issues, by resolving the turbulent flow structures, while the computational efficiency provides the ability to perform consistent uncertainty quantification.