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

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.