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SAM, a plant-level system analysis tool for advanced reactors (SFR, LFR,MSR/FHR) is under development at Argonne. As a modern system code, SAM aims toimprove the predictions of 3D flows relevant to reactor safety during transientconditions. In order to fulfill this goal, one approach is to implementmodeling of turbulent flow in SAM through establishing an embedded surrogatemodel for Reynolds stress/turbulence viscosity based on machine learningtechniques. The proposed approach is based on an assumption that there exists afunctional dependency relationship between local flow features and localturbulence viscosity or Reynolds stress. There have been very limited studiesperformed to validate this assumption. This paper documents a case study toexamine the assumption in a scenario of potential reactor applications. Thework doesn't aim to theoretically validate the assumption, but practicallyvalidate the assumption within the limited application domain. From themethodological point of view, the approach used in this paper could beclassified into the so-called Type I machine learning (ML) approach, where ascale separation assumption is proposed claiming that conservation equationsand closure relations are scale separable, for which the turbulence models arelocal rather than global. The CFD case studied in this work is a 3D transientthermal stratification tank flow problem performed using a Reynolds-averagedNavier-Stokes turbulence model in STARCCM+ code. Flow information of allgeometric points in all timesteps is collected as training data and test data.

Development of a Data-driven Turbulence Model for 3d Thermal Stratification Simulation during Reactor Transients