Session: 15-13

Paper Number: 136284

136284 - Les of Twin High-Re Rectangular Jets for Benchmarking Model Order Reduction Methods. 

Abstract: 

The study of turbulent mixing in non-isothermal coolant streams is crucial in understanding thermal striping, an oscillatory mixing pattern that can lead to thermal fatigue and degradation of internal components in advanced nuclear reactors. This phenomenon is prominent in sodium fast reactors due to the high conductivity and resultant large thermal gradients within the coolant. However this phenomenon has been observed in various reactor types, including light water reactors, highlighting the importance of predicting and mitigating thermal striping effects. The thermal striping phenomenon is linked closely to local 3D fine scales often associated with turbulence. In order to resolve such fine scales, historically, techniques such as Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS) were required for thermal striping prediction.

In our research, we leverage high fidelity data to construct reduced order models. Specifically, we utilize Proper Orthogonal Decomposition (POD) based Model Order Reduction (MOR) to replicate thermal striping behaviors derived from LES. Then, we focus on identifying the low-frequency mode representative of the thermal striping phenomenon. This approach allows us to efficiently capture the essential features of thermal striping while significantly reducing computational costs.

Our investigation centers on the mixing dynamics within the Reactor Cavity Cooling System (RCCS) separate effects test facility. We study the interaction of two high Reynolds number parallel plane jets within a confined plenum to uncover flow mixing characteristics. The particular system was chosen due to the inherit low frequency mode caused by recirculation of the flow in the confined geometry. Such a low frequency mode provides the perfect opportunity to study thermal striping characteristics. Initially, high fidelity LES simulations are employed to generate velocity and temperature statistics, as well as time series data for comparison against future reduced order model approaches. Analysis of the high fidelity velocity time series data using Power Spectrum Density (PSD) reveals a distinct low frequency mixing mode indicative of thermal striping.

Subsequently, we utilize Proper Orthogonal Decomposition (POD) to extract dominant flow structures from 600 high fidelity instantaneous velocity snapshots. These POD modes are then utilized to construct a reduced-order model (ROM) capable of replicating the low frequency mode associated with thermal striping. By leveraging reduced-order modeling techniques, we can efficiently simulate thermal striping phenomena while maintaining accuracy.

Overall, our research contributes to the advancement of predictive modeling for thermal striping in advanced nuclear reactors. By combining high-fidelity simulations with reduced-order modeling approaches, we aim to enhance our understanding of thermal striping dynamics and develop effective mitigation strategies for reactor safety and performance optimization.

Presenting Author: John Acierno Pennsylvania State University

Presenting Author Biography: John Acierno is a Ph.D. candidate at The Pennsylvania State University, under the guidance of Dr. Elia Merzari. His primary research revolves around conducting high-fidelity simulations for advanced reactors. John's expertise lies in characterizing the thermal striping phenomena within advanced reactor designs, with a specific emphasis on Sodium-cooled Fast Reactors (SFRs) and Gas-cooled Fast Reactors (GFRs).

Authors:

John Acierno Pennsylvania State University
Elia Merzari Pennsylvania State University
Victor Petrov University of Michigan
Annalisa Manera University of Michigan