Towards Thermal Striping Predictions Using LES-POD Data in Reduced-Order Models

The study of turbulent mixing in non-isothermal 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 or Direct Numerical Simulation have been required to resolve these fine scales. Our investigation focuses on the mixing dynamics within the Reactor Cavity Cooling System 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 and temperature statistics, along with time-series data, for comparison with Reduced-Order Model approaches. Power Spectrum Density 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 to extract the dominant flow structures from high-fidelity instantaneous velocity snapshots. These POD modes were then used to construct 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 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 a range of Reynolds numbers (Re = 100 to 10,000). By combining LES data with ROM techniques, we show that this approach offers a promising method for predicting the low-frequency modes linked to thermal striping.