  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 to
improve the predictions of 3D flows relevant to reactor safety during transient
conditions. In order to fulfill this goal, one approach is to implement
modeling of turbulent flow in SAM through establishing an embedded surrogate
model for Reynolds stress/turbulence viscosity based on machine learning
techniques. The proposed approach is based on an assumption that there exists a
functional dependency relationship between local flow features and local
turbulence viscosity or Reynolds stress. There have been very limited studies
performed to validate this assumption. This paper documents a case study to
examine the assumption in a scenario of potential reactor applications. The
work doesn't aim to theoretically validate the assumption, but practically
validate the assumption within the limited application domain. From the
methodological point of view, the approach used in this paper could be
classified into the so-called Type I machine learning (ML) approach, where a
scale separation assumption is proposed claiming that conservation equations
and closure relations are scale separable, for which the turbulence models are
local rather than global. The CFD case studied in this work is a 3D transient
thermal stratification tank flow problem performed using a Reynolds-averaged
Navier-Stokes turbulence model in STARCCM+ code. Flow information of all
geometric points in all timesteps is collected as training data and test data.
