The objective of this study is to assess the predictive capabilities of low and high-fidelity turbulence models for low-Prflows. For this purpose, predictions by two-equation (k-ε) Reynolds-Averaged Navier Stokes (RANS), partially-averaged Navier Stokes (PANS) hybrid RANS/Large Eddy Simulation (LES), and DSM, WALE, filtered LES models are compared for four different test cases, namely vertical channel flow, vertical backward facing step, flow over a rod bundle and heat transfer in ascending and descending flow through a pipe with a constant wall heat flux. The test cases involve a range of complex flow conditions including separation/reattachment and aiding and opposing buoyant forcing (Re ranging from 640 to 40K; Ri ranging from −0.65 to 0.65) for water (Pr = 0.71) and liquid metals (Pr = 0.00585 to 0.025) flows. The validation study demonstrates that turbulence models are 4% more accurate for higher Prflows that for low-Pr flows; 6% more accurate for forced convective conditions than for flows involving mixed convective conditions; and predict aiding buoyant flow conditions better than the opposing buoyant flow conditions. Overall, LES performed the best and provided averaged error of 6% followed by 10% by PANS and RANS showed the largest error of 14%.

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