NEAMS IRP challenge problem 2: Thermal striping of reactor Internals

• Published in: Nuclear engineering and design Vol. 433; no. C; p. 113879
• Main Authors: Baglietto, Emilio, Acierno, John, Manera, Annalisa, Nguyen, Quynh M., Petrov, Victor, Pham, Monica, Wang, Yu-Jou, Jin, Yue, Feng, Jinyong, Strasser, Wayne, Shaver, Dillon, Merzari, Elia
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2025; Elsevier
• Subjects: Data driven modeling; Hybrid turbulence modeling; Parallel jets; Thermal striping; Turbulent heat transfer; Parallel jets; Turbulent heat transfer; Data driven modeling; Hybrid turbulence modeling; Thermal striping
• ISSN: 0029-5493
• DOI: 10.1016/j.nucengdes.2025.113879

•The NEAMS IRP effort has assessed the accuracy and applicability of hybrid RANS models in support of thermal fatigue assessment.•Experimental data from the RCCS and DESTROJER facilities support rigorous assessment of CFD simulations.•High-fidelity LES simulation with NekRS provide upscaling of experimental results for turbulence modeling validation.•The STRUCT hybrid approach demonstrates LES like accuracy on URANS type meshes.•A two-level machine learning approach was demonstrated for reduced order modeling of thermal striping. Oscillatory mixing of non-isothermal liquid coolant streams in advanced reactors can lead to thermal fatigue damage to fuel and reactor components. The NEAMS IRP CP2 has been seeking the development of an accurate, yet computationally affordable, turbulence modeling option for thermal-striping predictions. Efficient mixing of coolant streams in upper internal structures, lower plena, and heat exchangers significantly impacts the design of these systems, as well as their operation, and maintenance. This challenge problem generalizes specific needs related to the TerraPower and General Atomics designs by developing a set of benchmarks to advance and quantify the accuracy of the thermal striping modeling approach. The focus of the activities is to advance and demonstrate a modeling practice capable of accurately representing the performance of the structural reactor components under the influence of thermal striping. Assessment against adiabatic quasi-2D jets striping has demonstrated great promise for the proposed turbulence approaches, with 2 orders of magnitude acceleration from the reference LES solution. More recent efforts have extended the quasi-2D validation to diabatic conditions, leveraging the demonstrated accuracy of the highly resolved LES methods, and with a new set of experimental data for heated parallel round jets. Further upscaling through the use of reduced order models is being evaluated, and a two-step machine learning approach has been demonstrated on thermal striping for 3 parallel jets.