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The European High Performance Computing Joint Undertaking (EuroHPC JU)

Towards low Prandtl number flows for Gen-IV reactor applications

Awarded Resources (in core hours)
Vega CPU
System Partition
1 November 2022 - 31 October 2023
Allocation Period

The thermo-fluid behavior of molten pools across wide-ranging orders of Rayleigh (Ra) and Prandtl (Pr) numbers is not yet fully understood and universal laws are still up for debate [Zhu et al., PRL, 2019].

Even more so is the interaction of different layers and other mechanisms that may exist in the pool. The phenomena can be found in nature such as the Earth’s mantle convection and in industrial processes. In nuclear engineering, a molten pool of heat-generating debris can form in the lower head of a reactor pressure vessel during a severe accident progression. In proposed Gen-IV reactors, molten pools are chosen due to its undeniable benefits.

Recently, we have successfully performed a Direct Numerical Simulation (DNS) of an internally heated molten pool with Ra=10^11 (the highest Ra in a hemispherical geometry to date) at different Pr numbers using the high-fidelity spectral-element code Nek5000. However, only moderate values of Pr numbers were run which applies to water or molten salt systems.

For low Prandtl number flows in confined curved geometries, which represent molten metal systems, the literature provides scarce information on high-fidelity simulations. But recently the scientific and technological interest is growing due to its application in Gen-IV reactors and Small Modular Reactors (SMRs), particularly the Lead-cooled Fast Reactors (LFRs) and Sodium-cooled fast reactors (SFRs).

The main objective of the proposed project is to employ our DNS approach and explore the low Prandtl number regimes.

Comparison of our results with known correlations relating the Nusselt number to the Rayleigh and Prandtl numbers will also be performed. Finally, the DNS results will be used as a reference data for Large Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS) model development or analysis.