A collaborative effort towards the accurate prediction of turbulent flow and heat transfer in low-Prandtl number fluids

Afaque Shams, Ferry Roelofs, Iztok Tiselj, Jure Oder, Yann Bartosiewicz, Mathieu Duponcheel, Bojan Niceno, Wentao Guo, Enrico Stalio, Diego Angeli, Andrea Fregni, Sophia Buckingham, Lila Koloszar, Augustin Villa Ortiz, Philippe Planquart, Chidambaram Narayanan, Djamel Lakehal, Katrien Van Tichelen, Wadim Jäger, Thomas Schaub

    Research outputpeer-review


    This article reports the experimental and DNS database that has been generated, within the framework of the EU SESAME and MYRTE projects, for various low-Prandtl flow configurations in different flow regimes. This includes three experiments: confined and unconfined backward facing steps with low-Prandtl fluids, and a forced convection planar jet case with two different Prandtl fluids. In terms of numerical data, seven different flow configurations are considered: a wall-bounded mixed convection flow at low-Prandtl number with varying Richardson number (Ri) values; a wall-bounded mixed and forced convection flow in a bare rod bundle configuration; a forced convection confined backward facing step (BFS) with conjugate heat transfer; a forced convection impinging jet for three different Prandtl fluids corresponding to two different Reynolds numbers of the fully developed planar turbulent jet; a mixed-convection cold-hot–cold triple jet configuration corresponding to Ri = 0.25; an unconfined free shear layer for three different Prandtl fluids; and a forced convection infinite wire-wrapped fuel assembly. This wide range of reference data is used to evaluate, validate and/or further develop different turbulent heat flux modelling approaches, namely simple gradient diffusion hypothesis (SGDH) based on constant and variable turbulent Prandtl number; explicit and implicit algebraic heat flux models; and a second order turbulent heat flux model. Lastly, this article will highlight the current challenges and perspectives of the available turbulence models, in different codes, for the accurate prediction of flow and heat transfer in low-Prandtl fluids.
    Original languageEnglish
    Article number110750
    Pages (from-to)1-20
    Number of pages20
    JournalNuclear Engineering and Design
    StatePublished - 1 Sep 2020

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