Toward Thermal Striping Predictions Using Large Eddy Simulation-Proper Orthogonal Decomposition Data in Reduced-Order Models

The study of turbulent mixing in nonisothermal coolant streams is crucial for understanding thermal striping, which can lead to thermal fatigue and degradation of internal components in advanced nuclear reactors. Thermal striping is closely linked to the fine 3D scales of turbulence. Historically, large eddy simulation (LES) or direct numerical simulation has been required to resolve these fine scales. Our investigation focuses on the mixing dynamics within the reactor cavity cooling system (RCCS) separate-effects test facility, where we examine the interaction of two parallel plane jets within a confined plenum. To begin, LES simulations were conducted to generate velocity statistics, along with time series data, for comparison with reduced order model (ROM) approaches. Power spectrum density (PSD) analysis of the velocity time series reveals a distinct low-frequency mixing mode, which is indicative of thermal striping. Next, we applied proper orthogonal decomposition (POD) to extract the dominant flow structures from high-fidelity instantaneous velocity snapshots. As a first step, POD modes were used to construct 2D ROMs that attempt to replicate the low-frequency mode associated with thermal striping. Sensitivity studies demonstrated that increasing the number of snapshots and POD modes improves 2D ROM accuracy, while also increasing the computational cost. To address this, we incorporated closure models and found that the constrained optimization ROM performed best across Reynolds numbers ranging from 100 to 10,000. By combining LES data with ROM techniques, we show that this approach offers a promising method for modeling the low-frequency modes linked to thermal striping.