Revisiting Reynolds and Nusselt numbers in turbulent thermal convection

In this paper, we extend Grossmann and Lohse’s (GL) model [S. D. Lohse, “Thermal convection for large Prandtl numbers,” Phys. Rev. Lett. 86, 3316 (2001)] the predictions of Reynolds number (Re) Nusselt (Nu) in turbulent Rayleigh–Bénard convection. Toward objective, use functional forms prefactors dissipation rates bulk boundary layers. The arise due to inhibition nonlinear interactions presence walls buoyancy compared free turbulence, along with a deviation viscous layer profile from Prandtl–Blasius theory. We perform 60 numerical runs on three-dimensional unit box range Rayleigh numbers (Ra) (Pr) determine aforementioned using machine learning. revised are better agreement past experimental results than those GL model, especially extreme numbers.