OpenFOAM® pp 93-108 | Cite as

Development of Data-Driven Turbulence Models in OpenFOAM\({^{\textregistered }}\): Application to Liquid Fuel Nuclear Reactors

  • M. Tano-RetamalesEmail author
  • P. Rubiolo
  • O. Doche


The following chapter presents a new approach for the development of turbulent models, with potential application to the design of liquid fuel nuclear reactors. To begin the chapter, the work being carried out at LPSC (Grenoble) for validating the modeling of molten salt coolants is presented, alongside a Backward-Facing Step (BFS) geometry, which will be studied throughout this work. In the subsequent section, various turbulence models are evaluated in the BFS and their advantages and limitations are analyzed, with the conclusion that some improvements in the turbulence modeling are necessary. Therefore, the next section introduces a methodology for developing a nonlinear closure for turbulence models by means of Symbolic Regression via Genetic Evolutionary Programming (GEATFOAM). Then, this new methodology is implemented for direct numerical simulation data of the BFS, obtaining a new nonlinear closure for the standard k\(\varepsilon \) model. Finally, the new model is compared against classical turbulence models for the BFS, and, then, the extrapolability of this model is analyzed for available experimental data of an axial expansion in a pipe. Encouraging results are obtained in both cases.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University Grenoble Alpes, CNRS, Grenoble INP, LPSCGrenobleFrance
  2. 2.University Grenoble Alpes, CNRS, Grenoble INP, SIMAPGrenobleFrance

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