The influence of thermal boundary conditions on turbulent forced convection pipe flow at two Prandtl numbers

Different types of thermal boundary conditions are conceivable in numerical simulations of convective heat transfer problems. Isoflux, isothermal and a mixed-type boundary condition are compared by means of direct numerical simulations (for the lowest Reynolds number) and well-resolved large-eddy simulations of a turbulent forced convection pipe flow over a range of bulk Reynolds numbers from Re-b = 5300 to Re-b = 37700, at two Prandtl numbers, i.e. Pr = 0.71 and Pr = 0.025. It is found that, while for Pr = 0.71 the Nusselt number is hardly affected by the type of thermal boundary condition, for Pr = 0.025 the isothermal boundary condition yields approximate to 20% lower Nusselt numbers compared to isoflux and mixedtype over the whole range of Reynolds numbers. A decomposition of the Nusselt number is derived. In particular, we decompose it into four contributions: laminar, radial and streamwise turbulent heat flux as well as a contribution due to the turbulent velocity field. For Pr = 0.71 the contribution due to the radial turbulent heat flux is dominant, whereas for Pr = 0.025 the contribution due to the turbulent velocity field is dominant. Only at a moderately high Reynolds number, such as Re-b = 37700, both turbulent contributions are of similar magnitude. A comparison of first- and second-order thermal statistics between the different types of thermal boundary conditions shows that the statistics are not only influenced in the near-wall region but also in the core region of the flow. Power spectral densities illustrate large thermal structures in low-Prandtl-number fluids as well as thermal structures located right at the wall, only present for the isoflux boundary condition. A database including the first- and second-order statistics together with individual contributions to the budget equations of the temperature variance and turbulent heat fluxes is hosted in the open access repository KITopen (DOI : https: //doi.org/10.5445/IR/1000096346). (C) 2019 Elsevier Ltd. All rights reserved.